Recent research shows that the share of organizations that use AI-based test automation tools as a part of the testing process. Moreover its usage has increased over the past year by more than a quarter, 72% compared to 55% previously. Such a rise emphasizes the importance of the AI-based test automation tools. AI enhances everything from test creation and test execution to regression testing and test maintenance.

This article will examine the top 15 best AI tools for QA automation, and examine their features, benefits and actual use cases. We will also explore the specifics of these best AI automation tools in detail so you can know which ones are most suitable to your team.

The Role of AI in QA Automation

It is not a secret that AI for QA is significant. However, it is worth knowing why it is so. AI in QA automation is transforming the way test management and test coverage are being addressed by teams.

Speed and Efficiency in Test Creation and Execution

Among the most critical advantages of the AI test automation tools is the speed with which they will generate and run the test cases. Conventional test creation systems take place in labor-intensive, manual procedures that are error-prone and can overlook scenarios. Automating QA with generative AI and natural language processing, means that automation tools for QA can create test scripts within seconds based on user stories, Figma designs or even salesforce data.

Enhanced Test Coverage and Reliability

AI testing tools such as Testomat.io will help to ensure tests are provided in all corners of the application. Using prior test data and employing the machine learning algorithms, AI automation testing tools are able to find edge cases and complex situations humans may not consider. This contributes towards improved test results and increased confidence over the software performance.

Reduced Test Maintenance and Adaptability

The other b advantage of AI-based test automation tools is that they evolve when an application is changed. The idea of self-healing tests is revolutionary in regards to UI changes. Instead of manually updating test scripts each time, AI is used to test automation tooling to adjust tests to reflect changes, making them much easier to maintain.

Top 15 AI Tools for QA Automation

Let’s explore the best AI tools for QA automation that can help your team take the testing to the next level.

1. Testomat.io

Testomat.io is focused on the simplification of the whole process of testing and test automation. Set up, run, and analyze tests with AI on this test management platform.

Key Features:

• Generative AI for Test Creation: Rather than take hours micromanaging test script creation, Testomat.io uses it via user stories and architected designs. It is time-saving and accurate.
• AI-Powered Reporting: Once the tests are performed, the platform will provide you a clear, actionable report. Testomat.io can automate manual tests, you can also ask their agent to generate a piece of code\scripts to automate scenarios for the needed testing framework.
• Integration with CI/CD Pipelines: Testomat.io seamlessly integrates with CI/CD tools such as Jira, GitHub, GitLab, so it is a good choice of tool used by teams with preexisting CI/CD pipelines.

Why it works: Testomat.io removes the headache of test management. Automating the process of creating the test with AI will allow you to build and grow your automation inputs without being slowed down by manual processes. It is like having a teammate that does all the heavy tasks and freeing your team to concentrate on what is really important, creating quality software more quickly.

2. Playwright

Playwright is an open-sourced automation testing tool to test web applications on all major browsers, as well as Playwright MCP.

Key Features:

• Cross-Browser Testing: Supports Chrome, Firefox, and WebKit to test your app across different modern platforms.
• Parallel Execution: Tests can be performed simultaneously on multiple browsers instead of having to run each test individually, which saves time.
• AI Test Optimization: Possible only with third-party solutions. AI helps the Playwright to prioritize the tests based on the history of the past tests.

Why it works: AI optimization and parallel execution allows your QA teams to test wider territories in shorter execution time and this is of utmost importance in the context of modern software development life-cycle.

3. Cypress

Cypress refers to an end-to-end testing framework that can be used to test web applications with the use of AI so as to provide immediate feedback.

Key Features:

• Instant Test Results: The results of tests are provided on-the-fly since it is JavsScript-based, so it is easy to setup.
• AI-Powered Test Selection: It selects the most pertinent test steps to run on the basis of the record of prior examinations.
• Real-Time Debugging: There is faster diagnosis to fix the problem.

Why It Works: By enabling teams to test fast and get real-time insight into the process, Cypress streamlines the testing process and improves the user experience by enabling teams to deliver reliable and bug-free software much quicker.

4. Healenium

Healenium is a self-healing AI based tool which enables testing scripts to automatically adapt to changes initiated on the UI side, thus leading to adequate profoundness of regression testing.

Key Features:

• Self-Healing: Automatically fixes broken tests caused by UI changes.
• Cross-Platform Support: Works across both web applications and mobile applications.
• Regression Testing: Provides continuous, automated regression testing without manual intervention.

Why It Works: The self-healing capability of Healenium will free your QA engineers to not need to manually update test scripts when the UI changes. This saves on maintenance work and that your tests continue to be effective.

5. Postman

Postman is the most commonly used application in API testing and the tool employs AI to facilitate the process of testing and optimization.

Key Features:

• Smart Test Generation: Automatically creates API test scripts based on input data and API documentation.
• AI Test Optimization: Identifies performance bottlenecks in API responses and suggests improvements.
• Seamless CI/CD Integration: Integrates with CD pipelines to automate testing during continuous deployment.

Why It Works: The use of the Postman AI abilities enables working teams to test as well as optimize API performance with relative ease, as this login will guarantee faster, reliable services in the course of transitioning to production.

6. CodeceptJS

CodeceptJS is an end-to-end friendly testing framework that incorporates AI as well as behavior-driven testing to simplify end-to-end testing and make it effective. The solution is ideal to teams that need to simplify their test automation without forfeiting capacity.

Key Features:

• AI-Powered Assertions: AI enhances test assertions, making them more accurate and reliable, which improves the overall testing process.
• Cross-Platform Testing: Whether it’s a mobile application or a web application, CodeceptJS runs tests across all platforms, ensuring comprehensive test coverage with minimal manual work.
• Natural Language for Test Creation: With natural language processing, you can write test cases in plain English, making it easier for both QA teams and non-technical members to contribute.

Why It Works: CodeceptJS is flexible and fits into turbulent changes that occur in the software development processes. It can be incorporated with CI/CD pipelines easily, allowing your team to deploy tested features within the shortest time without being worried about broken code. It can be integrated with test management platforms as well, providing a complete picture of teamwide test efforts to teams.

7. Testsigma

Testsigma is a no-code test automation platform that uses AI to help QA teams automate testing for web, mobile, and API applications.

Key Features:

• No-Code Test Creation: Build test cases by using an easy interface without writing any code.
• AI-Powered Test Execution: Efficiently executes test steps to complete test cases as fast as possible with greater accuracy.
• Auto-Healing Tests: Auto-adjusts tests to UI changes, and thus minimize maintenance work.

Why It Works: For less technical based teams, Testsigma would provide a simple methodology to enter the realm of automated testing with its artificial intelligence driven optimisations making sure that the test outcomes are excellent.

8. Appvance

Appvance is an AI-powered test automation platform that facilitates the web, mobile, and API testing.

Key Features:

• Exploratory Testing: Utilizes AI to help discover paths through applications, and generate new test cases.
• AI Test Generation: Generates tests automatically depending on the past behavior on the application.
• Low-Code Interface: Has low-code interface so it is accessible to a variety of users, both technical and non-technical.

Why It Works: Exploratory testing with AI will uncover paths that may not be visible by humans who will do testing hence ensuring that the most complex of testing scenarios is covered.

9. BotGaug

BotGauge is an AI-powered tool, geared towards functional and performance testing of bots, to ensure that they are not only functional, but behave well in any environment.

Key Features:

• Automated Test Generation: Creates functional test scripts for bots without manual effort.
• AI Performance Analysis: Analyzes bot interactions to identify performance bottlenecks and areas for improvement.

Why It Works: BotGauge simplifies bot testing, rendering it more efficient and accelerating the deployment. It has AI-driven analysis that makes the bots go to production with a minimum delay.

10. OpenText UFT One

The OpenText UFT One solution allows teams to develop front-end and back-end testing, accelerating the speed of testing with the use of AI based technology.

Key Features:

• Wide Testing Support: Covers API, end-to-end testing, SAP, and web testing.
• Object Recognition: Identifies application elements based on visual patterns rather than locators.
• Parallel Testing: Speeds up feedback and testing times by running tests in parallel across multiple platforms.

Why It Works: With automation of test maintenance and the elevated precision of AI, OpenText UFT One gets QA teams working more quickly without compromising quality. Its endorsement of cloud-based mobile testing promises scalability and reliability.

11. Mabl

Mabl is an AI-powered end-to-end testing which makes it easy to use behavior-driven design to test.

Key Features:

• Behavior-Driven AI: Automatically generates test cases based on user behavior, reducing manual effort.
• Test Analytics: Provides AI insights to help optimize test strategies and improve overall test coverage.

Why It Works: Mabl removes the time and effort of testing by automating many of the repetitive elements in the testing process and infuses into existing CI/CD pipelines.

12. LambdaTest

With increased efficiency, LambdaTest is an AI-driven cross-browser testing platform capable of running testing of web application across browsers in a much faster and accurate manner.

Key Features:

• Visual AI Testing: Finds and checks visual errors in several browsers and devices.
• Agent-to-Agent Testing: This facilitates testing of the web applications with AI agents that plan and execute more successfully.

Why It Works: LambdaTest allows QA teams to conduct multi-browser testing with greater ease, accuracy and quicker which results in detecting visual defects at the earliest. Its analyst-in-the-loop validation will result in a stable performance in diverse settings.

13. Katalon (StudioAssist)

Katalon is a wide range of test automation tools that come with AI for faster and better testing.

Key Features:

• Smart Test Recorder: Automates test script creation, making it easier for QA teams to get started.
• AI-Powered Test Optimization: Suggests improvements to your test scripts, increasing test coverage and performance.

Why It Works: Katalon Studio speeds up the test development process and reduces manual workload that an engineer needs to accomplish by providing them with actionable feedback, thus making it a trusted tool between QA engineers and developers.

14. Applitools

Applitools specializes in the visual AI testing, such as the UI domains, and whether the page could look and work as it should on the various platforms.

Key Features:

• Visual AI: Detects UI regressions and layout issues to ensure your app looks great across browsers and devices.
• Cross-Browser Testing: AI validates your app’s performance across multiple browsers and devices.

Why It Works: In increasing velocity, Applitools promotes UI testing through visual testing, which is an AI-powered tool to reveal visual defects at the beginning of the cycle. It is ideal when teams require UI test coverage.

15. Testim

Testim is an AI-powered test automation platform to accelerate test development and execution of web, mobile and Salesforce tests.

Key Features:

• Self-Healing Tests: Automatically adjusts to UI changes, reducing the need for manual updates.
• Generative AI for Test Creation: Generates test scripts from user behavior, minimizing manual efforts.

Why It Works: Testim can automatically respond to change within the application, decreasing maintenance costs. The speed of test execution is accelerated by this AI-enabled flexibility, thus realization time of development cycles is also quick.

Top 15 AI Tools for QA Automation: Comparison

Conclusion

The future of AI in QA automation holds great potential as AI integration will continue to be an important part in test execution in software testing. Regardless of what you want to achieve – automate your regression testing, improve test coverage, or reduce test maintenance, AI-enhanced tools such as Testomat.io, Cypress, and Playwright can be a solution to the problem.

The best AI automation tools allow teams to test smarter, faster, and more reliably. As software development continues to accelerate, integrating AI-based test automation tools will help ensure that your applications are not only functional but also scalable and user-friendly. The time to embrace AI for QA is now.

The post The Best 15 AI Tools for QA Automation in 2025: Revolutionizing Software Testing appeared first on testomat.io.

What is enterprise application software, really? Think of it as the digital backbone of large organizations. Such enterprise applications manage everything such as payrolls, inventory management, etc, and often thousands of users access them at once, processing sensitive data of millions of dollars worth. A malfunction impacts an individual, and it can unglue the entire operation and have a devastating effect on customer experience.

The fact is that testing enterprise applications demands an entirely new strategy in comparison with smaller projects. You have got wildly complex integrations, really tight regulatory compliance and testing requirements that would lead most quality assurance teams into a cold sweat. This is where dedicated enterprise testing software such as Testomat.io comes in, because real-world enterprise level operations require all the features that only such a software can bring to the table.

The Real Challenges of Testing Enterprise Applications

Enterprise testing is a beast of a different nature. We’re not talking about a few hundred test cases here. A typical enterprise software system might have tens of thousands of test cases spread across dozens of modules.

How Testomat.io Tackles Enterprise QA Challenges Head-On

The approach of Testomat.io to the scale issue is smart organization features which make sense when dealing with large operations. Instead of forcing teams to work with rigid structures that don’t match their reality, the platform allows flexible organization through tags, suites, and folders that mirror how enterprise applications are actually built and maintained, supporting various types of enterprise software applications.

The cross-project visibility aspect solves one of the largest enterprise application testing headaches which is, what is going on in other groups and sections. Software test management professionals will be able to monitor progress of numerous projects at the same time and different projects highlight areas of problems and knowledge of where crucial integration points should be observed and to cover sufficient testing ground.

The search and filtering functions allow substantial amounts of time to be saved treating thousands of test cases. Instead of scrolling through a never-ending list in the hopes of finding what they are looking for, quality assurance teams would be able to narrow down what they need within a few clicks, by way of tags, requirements, or any other customizable attribute that would make sense to their organization. This business testing tool methodology will maintain a high quality of software and it will increase efficiency

Seamless CI/CD Pipeline Integration

The native connectivity into widely-used CI/CD testing automation software (such as Jenkins, GitHub Actions and GitLab CI) is also available. These integrations are automatic and thus these make no need of frequent maintenance or configuration upgrades throughout the development phase.

The integration is carried out on a real-time basis hence test results are made available instantly after running a test allowing swift decisions to be made concerning deployment of codes. In enterprise applications, where deployment windows may be somewhat fixed to a maintenance window, a faster system may mean the difference between meeting business requirements, and incurring stakeholder disappointment without any discontinuity in business itself.

The capability to invoke enterprise-level test runs within the pipeline support advanced test strategy options. The various test suites can be set to run in different teams according to the nature of changes being deployed, in that no wastage of resources on unnecessary tests will be done. This ability in test automation it allows both manual and automated procedures.

Continuous Testing Strategies

Enterprise apps can make use of continuous testing methods whereby feedback is given continuously about the functionality of the system. Among these is automatic regression testing which can be carried out outside the day’s work and so there will be no negative effects on the productivity of development teams since potential problems can be caught instantly without wasting the efforts of the individuals.

Effective continuous testing also includes intelligent alerting that notifies appropriate team members when issues occur without creating notification fatigue. The alerting system should be configurable to match organizational structure and escalation procedures, ensuring that critical issues get immediate attention while routine matters are handled through normal channels, supporting overall business continuity and project management goals.

Comprehensive Traceability and Reporting

Enterprise applications require detailed traceability between business requirements, test cases, and code changes. Testomat.io provides robust linking capabilities that connect all these elements, enabling teams to understand the business impact of test failures and prioritize fixes based on actual business value while ensuring functional requirements are met.

The customizable reporting features provide insights that enterprise teams actually need – test coverage metrics, identification of flaky tests that cause unnecessary delays, and trend analysis that reveals patterns in software quality over time. These analytics help teams make data-driven decisions about where to focus their testing efforts and how to improve overall efficiency while tracking key metrics for project management.

BDD and Gherkin produce business readable test examples that bridge the communication gap between tech and business teams. For enterprise applications where business logic can be incredibly complex, this capability ensures that subject matter experts can validate that tests actually cover the scenarios that matter most to the organization, supporting functional testing and application testing needs.

Enterprise-Grade Collaboration Features

The platform also supports collaboration by allowing multiple persons to join in shared dashboards that provide real-time view of test execution and results. The information is available to all stakeholders, including QA engineers, product managers, business analysts among others, and they do not need to be equipped with technical knowledge to understand the outcomes of the tests being performed, and that is improving customer experience with the testing process.

Role-based access control mitigates the threat of sensitive information and testing information being obtained by the wrong parties as well as allowing collaboration as necessary. It is essential to an enterprise that uses regulated data or has proprietary business processes.

Access controls can be tailored to suit your exact organizational hierarchy and safety precisions, to be sure of regulatory compliance and comply with industry regulations.

Proven Best Practices for Enterprise Testing Success

The effective enterprise testing strategies should be open to both shift-left and shift-right tactics. Shift-left testing has quality testing activities earlier in the development process when costs are lower to correct. This also involves such reviews as requirements, design validation and early development of test automation scripts.

Shift-right testing extends quality assurance into production environments through monitoring, user experience, feedback analysis, and production testing strategies. In the case of enterprise applications, these may include synthetic transaction verification that ensures critical business processes logs 24/7; performance test monitoring that follows system behavior under real-load conditions which is also supported by the rapid crash recovery system and live support.

Smart Test Data Management

Enterprise applications often require large volumes of test data that accurately represent realistic business scenarios. Creating and maintaining this data can be expensive and time-consuming, especially when dealing with complex business rules and data relationships across supply chain operations and other critical processes.

Effective test data strategies emphasize reusability, enabling teams to efficiently validate different scenarios without duplicating data creation efforts. This becomes particularly important when testing different devices or compatibility testing scenarios that require the same underlying business data while ensuring comprehensive application coverage.

Privacy and security considerations add another layer of complexity to test data management. Teams need strategies for creating realistic test data that doesn’t expose sensitive data or violate regulatory requirements. This might include data masking techniques, synthetic data generation, or carefully controlled access to sanitized production data subsets that maintain data security while supporting thorough testing. There are also functions like Version Control, Branges, History Archive, Reverting changes, Git integration.

Leveraging AI for Intelligent Testing

Modern enterprise testing benefits from artificial intelligence capabilities that can analyze patterns, suggest test scenarios, and identify high-risk areas based on code changes and historical data. These intelligent features help teams focus their testing efforts where they’re most likely to find issues or where failures would have the highest business impact on customer experience.

AI-powered test generation can create comprehensive test suites more efficiently than manual testing approaches, while intelligent analysis of test results helps identify patterns that might not be obvious to human reviewers.

Testomat.io’s Enterprise Plan: Built for Scale

Most of the abilities, which large firms need to handle extensive test management, are covered in the Enterprise Plan. The pay-per-user payment system and unlimited projects will allow organizations to ramp up their testing activities without project based restrictions which may limit the scope of the tests unnecessarily.

• Security options contain Single Sign-On integration and SCIM support to facilitate automated user provisioning, so that access control is properly in line with corporate security measures. The self-hosted deployment adds data sovereignty and the extra security that an organization may need to its needs in areas where a high level of data handling is required.
• The enhanced AI functions such as test generation and suggestion support help teams to generate a thorough test coverage more productively. Using AI-equipped requirements management allows many organizations to retain traceability between their business requirements and testing activities, and the utilization of custom AI providers allows adoption into the preferred tools within organizations.
• The platform provides means to work with branches and versions to handle different releases and environments when it comes to testing. Bulk user management is convenient when an organization has many users, whereas granular role-based access controls allow dividing organizations into various roles and giving them corresponding rights.
• The cross-project analytics allows seeing the picture of testing effectiveness of the whole organization, allowing the leadership to understand its maturity and see areas of improvement. This platform can support even large enterprise applications based on up to 100,000 tests.
• Complete audit trails and SLA promises give enterprises the documentation and integrity that they need to support compliance initiatives and organizational confidence.

Ready to Transform Your Enterprise Testing?

Testomat.io provides the capabilities that enterprise organizations need to manage testing at scale while maintaining the quality and reliability that business operations require. The platform’s combination of intelligent organization, automation support, and collaboration features addresses the key challenges that enterprise testing teams face every day.

Consider evaluating how Testomat.io’s enterprise features could address your specific testing challenges. The flexibility of the platform allows it to be tailored to your organizational processes but will provide the standardization required to afford collaboration across large and distributed teams.

Enterprise onboarding support provides seamless implementation and swift adoption, with teams able to see tangible value now and lay the foundation of a broad and long term testing platform able to support ongoing business growth and innovation.

The post Enterprise Application Testing: How Testomat.io Powers QA appeared first on testomat.io.

To resolve these problems, you can consider database testing tools, which can be your solution. In this article, we’ll break down what database testing is, the key types of testing, when and why the best database testing tools are needed, and how to choose the right one for your needs.

What is database testing?

To put it simply, database or DB testing is applied to be sure that databases function correctly and efficiently together with their connected applications. The mentioned process verifies the system’s data storage capabilities and retrieval functions and data processing efficiency while keeping consistency during all operations.

Let’s consider an example: The software testing process for new user sign-ups starts with database verification of correct information entry. The testers would run a specific SQL query to confirm that the users table received the new record and that the password encryption worked correctly.

Checking that their user ID correctly links to newly generated user profile records can be done by executing a join query to verify data consistency between the user_profiles and users tables.

The testers would also attempt to create a new account with an existing email address to validate database integrity; they would follow business rules for unique data to verify that the database correctly rejects the request and prevents a copy of the record.

Types of Databases: What are They?

The existence of multiple types of databases stems from the fact that no Information system can fulfill all requirements for every web application. Each database system has its own purpose to manage particular data types while addressing specific business requirements. The different database types exist because they meet specific company needs, which include data structure management and large-scale system requirements, performance, and consistency standards.

• Relational Databases or SQL Databases. They are known as the most common type, in which tables are used to organize data for easy data management and retrieval. Each table consists of rows and columns, where rows are records, and columns represent different attributes of that data.
• NoSQL Databases. They are designed to work with large and unstructured data sets and do not rely on tables. These databases are a good option for big data applications such as social media and real-time analytics because they support flexible data management of documents and graphs.
• Object-Oriented Databases. They store data as objects which follows object-oriented programming principles to eliminate the need for a separate mapping layer, thus simplifying development.
• Hierarchical Databases. This type arranges data in a tree-like structure, where each record has a parent-child relationship with other records, and forms a hierarchy. Thanks to this structure, it is easy to understand the relationships between data and access. These databases are used in applications that require strict data relationships.
• Cloud Databases. These databases keep information on remote servers, which can be accessed via the internet. This type provides scalability, where you can adjust resources based on your needs. Because they can be either relational or NoSQL, cloud databases are a flexible solution for businesses with global teams or remote users who need universal access to data.
• Network Databases. Based on a traditional hierarchical database, these databases provide more complex relationships, where each record can have multiple parent and child records, and form a more flexible structure. This type is suitable if there is a need to represent interconnected data with many-to-many relationships.

When And Why Should We Conduct Database Testing?

A fully functional database is essential for the adequate performance of software applications. It is utilized to store and create data corresponding to its features and respond to the queries.

However, if the data integrity is impacted, it can cause a negative financial impact on the organization. This is because data integrity leads to errors in decision-making, operational inefficiencies, regulatory violations, and security breaches.

Thus, performing database testing to handle and manage records in the databases effectively is a must for everyone – from the developer who is writing a query to the executive who is making a decision based on data. Before investing in a software solution, let’s review why you need to conduct quality assurance for your databases:

#1: Pre-Development

Making sure the database is built correctly and meets the project’s goals is critical to avoiding problems later. Testers need to check the schema design to be sure tables are set up properly, and they should check normalization to avoid storing the same information in multiple places.

Also, quality assurance specialists shouldn’t forget to verify constraints and indexing to implement data rules and guarantee good performance later.

#2: Before Going Live

The system requires complete verification for datasets to occur immediately before its launch to guarantee perfect functionality between the database and application, which results in a reliable first-day experience for users. The test process should validate fundamental operations (create, read, update, delete) in databases and verify stored procedures and triggers for errors.

#3: Migration of Data

The process of verifying datasets quality during migration guarantees that the information flows correctly and without error. The main goal at this point is to verify that migration does not create errors, which include missing records, corrupted values, or mismatched fields, and maintains the same information as the old one.

#4: Updates and Changes

If there have been patching, upgrading, and structural changes in the database, it creates potential risks for existing system functionality. So, it is mandatory to verify that new modifications do not interfere with current operational processes or generate unforeseen system errors.

The main priority should be to perform regression tests on queries and triggers and views, and dependent web applications. The re-validation process enables testers to verify that both existing and new features operate correctly, which maintains system stability throughout each update cycle.

#5: Security and Compliance

You need to give immediate attention to security measures and compliance standards in order to protect sensitive data, which is kept in databases. You need to stop illegal access and data breaches, make sure that it adheres to important regulations (for example, GDPR and HIPAA). Verification of permissions, encryption, and testing for SQL injection attacks are necessary to protect the datastore from hackers, build customer trust, and prevent your company from legal and financial risks.

#6: Data Consistency and Integrity

The verification of database stability requires ongoing checks to guarantee data accuracy and consistency, even when your datastore appears stable. Your business will face major problems when small errors, such as duplicated entries or broken data links, occur.

Types of Database Testing

Structural

This type aims to verify that the database’s internal architecture is correct. It helps to validate the operational functionality of database systems and check all the hidden components and elements, which are not visible to users (tables and schemas).

Functional

The purpose of functional testing is to verify how a database operates on user-initiated actions, including form saving and transaction submission.

• White box. It helps analyze the database’s internal structure and test database triggers and logical views to ensure their inner workings are sound.
• Black box. It helps test the external functionality, such as data mapping and verifying stored and retrieved data.

Non-Functional

• Data Integrity Testing. Thanks to this type of testing, you can verify that information remains both accurate and uniform throughout the database. Also, you can check loss and duplication of datasets to keep information as reliable and trustworthy as possible.
• Performance Testing. The evaluation of the performance of databases takes place under different operational conditions and evaluates the database’s response time, throughput, or resource utilization through load testing and stress testing.
• Load Testing. This type aims to accurately assess how the database will perform under real-life usage. It can be done by checking a database’s speed and responsiveness and simulating realistic user traffic.
• Stress Testing. This extreme form of load testing pushes a database to its breaking point. It evaluates the database’s performance by hitting it with an unusually large number of users or transactions over an extended period. The test helps identify boundaries while showing performance problems that happen when the system is under high stress.
• Security Testing. This type is applied to identify database vulnerabilities while confirming protection against unauthorized access and information leaks. The system requires verification of role-based access controls to be sure that users with particular roles can only access and perform authorized actions, which protects the entire system.
• Data Migration Testing. It is used to reveal problems that occur when information moves between different system components to ensure its integrity, accuracy, and completeness.

When to Use Database Testing Tools?

Let’s explore when you can use database testing tools:

What Are The Types Of Database Testing Tools For QA?

Let’s overview the types of tools used for database testing.

General Database Testing & Database Automation Testing Tools

These tools enable automated functional testing of databases to verify schemas and stored procedures, and triggers, and data integrity and CRUD operations (Create, Read, Update, Delete). They ensure repeatable, consistent tests, especially after frequent updates or deployments, and are used for:

• Unit testing SQL queries or stored procedures.
• The process of validating database logic matches the business rules that need to be followed.
• Regression testing after schema changes.

Database Performance Testing Tools & Database Load Testing Tools

These tools enable the simulation of real-world loads and traffic on a database to test its performance under stress conditions and concurrent user loads and large datasets. They are applied for:

• Stress testing queries under thousands of concurrent users.
• Checking query response times under peak load.
• Capacity planning before scaling infrastructure.

Database Migration Testing Tools

The tools ensure information movement between systems while checking record counts and data mappings, and referential integrity. They help to prevent data loss, corruption, and compliance issues. You can choose them if you need to:

• Verify migration of the records during cloud adoption.
• Check schema compatibility after upgrades.
• Guarantee the integrity of records after migration.

SQL Injection & Security Testing Tools

These tools allow you to focus on database security while detecting SQL injection vulnerabilities and weak permissions, and unencrypted data. They are helpful in the following cases:

• Identifying SQL injection risks in queries.
• Checking access controls, roles, and permissions.
• Validating encryption and security compliance.

Overview Of The Best Database Testing Tools

SQL Test (for SQL Server databases)

It is an easy-to-use database unit testing tool to generate a real-world workload for testing, which can be used on-premises as well as in the cloud. The tool integrates with major databases to offer a complete unit test framework which supports different database testing requirements. The learning curve for this tool is easy for SQL developers who already know SSMS.

• Key Features: Integrates with SQL Server Management Studio (SSMS), allows unit testing of T-SQL stored procedures, functions, and triggers.
• Common Use Cases: Ad-hoc data checks, data integrity audits, regression testing, and post-migration data validation.
• Best for: Developers and QA engineers who need quick, flexible, and precise control over their data checks without relying on a third-party tool.
• Pros: The system provides flexibility and does not require external tools while allowing direct control.
• Cons: SQL Server–only, limited scope beyond unit tests.

NoSQLUnit (NoSQL-specific Testing)

Used as a framework for validation of NoSQL databases to make sure that a database is in a consistent state before and after a test runs. The learning curve for this tool is medium because it needs Java/JUnit programming skills.

• Key Features: JUnit extension for NoSQL databases (MongoDB, Cassandra, HBase, Redis, etc.), data loading from external sources.
• Common Use Cases: Unit and integration testing for applications built on NoSQL databases.
• Best for: Java teams working with diverse NoSQL technologies.
• Pros: The tool provides support for multiple NoSQL databases and includes automated features for test data setup and teardown.
• Cons: Java dependency, not beginner-friendly for non-Java developers.

DbUnit (Java-based)

It is a Java-based extension for JUnit that’s used for database-driven verification, aiming to put the database in a known state between each test run. It helps to make sure that the tests are repeatable and that results aren’t affected by a previous test’s actions. The learning curve for this tool is moderate because it needs knowledge of JUnit and XML.

• Key Features: JUnit extension for relational DB testing, XML-based datasets, integration with continuous integration (CI) pipelines.
• Common Use Cases: Unit and integration testing for Java applications, especially for ensuring that business logic correctly interacts with the database.
• Best for: Java applications with relational databases.
• Pros: Well-established, CI/CD friendly, good for regression.
• Cons: The system has the following disadvantages: Verbose XML datasets, less intuitive for beginners, and Java-only.

DTM Data Generator

It is a user-friendly test data generator for creating large volumes of realistic test data, which helps testers fill a database with a huge amount of information for performance and load tests. The learning curve for this tool is easy to moderate and requires setup for complex rules.

• Key Features: Generates synthetic test data, customizable rules, and supports multiple databases.
• Common Use Cases: Populating databases with large datasets for running tests.
• Best for: Teams needing bulk test data quickly.
• Pros: Fast data creation, supports constraints and relationships.
• Cons: Paid license for full features, not suitable for dynamic/continuous test data generation.

Mockup Data

The data generation tool creates a genuine datastore and application test data, which improves data quality and accuracy while identifying data integration and migration problems. The learning curve for this tool is easy.

• Key Features: Random data generator with templates, custom rules, and quick CSV/SQL export.
• Common Use Cases: Creating sample data for demos, prototypes, and quality assurance (QA) environments.
• Best for: Developers/testers who need small to medium-sized datasets.
• Pros: Quick setup, customizable data, export flexibility.
• Cons: The system has limited scalability for very large datasets and is less suited for complex relational logic.

DataFaker

It is a Java and Kotlin library designed to streamline test data generation to populate databases, forms, and applications with a wide variety of believable information—such as names, addresses, phone numbers, and emails, without using real, sensitive information.

• Key Features: Open-source library for generating fake data (names, addresses, numbers, etc.), supports Java/Python. The learning curve for this tool is moderate and requires programming to configure.
• Common Use Cases: Generating realistic test data for applications and database validation.
• Best for: Developers comfortable with code-based test data creation.
• Pros: Open-source nature, flexibility, high customizability, and realistic datasets.
• Cons: The system requires coding skills and does not have a graphical user interface, and may need additional work for relational data.

Apache JMeter

The most popular performance testing tools, which can also be used for performance DB testing, simulate multiple users accessing the system, executing SQL queries, and monitoring response time. The learning curve for this tool is moderate, but complex for advanced scenarios.

• Key Features: Open-source load testing tool, supports JDBC connections, simulates heavy user loads on databases.
• Common Use Cases: Performance and stress testing databases, analyzing query response times.
• Best for: QA teams needing performance validation at scale.
• Pros: The platform offers free access and flexibility, b community backing and supports multiple information systems.
• Cons: The system requires advanced technical knowledge to establish, and it operates with more complexity than basic data generators.

How to Choose the Right Tool For Database Testing

To choose the right tool for the QA process, you must first define your goals. Your purpose for testing will determine which tools you need to use. Whether you need to validate schemas, queries, and stored procedures, test a database’s performance under heavy load, data migrations, integrity, or vulnerabilities, you should know it from the start.

Know Your Database Type and Match Tool to It

The database type determines the quality assurance strategy and test plan because relational and NoSQL databases require different QA techniques. So you should select a tool, which is designed to work with a specific structure of the datastore to ensure accurate and effective QA.

Choose A Tool That Matches The Skills Of Your Teams

A database testing tool is only as effective as the team using it, so you must choose one that matches their existing skill set. A complex tool (from the best database testing tools list) chosen for a team that uses graphical interfaces will create a long learning process, which will delay the project’s completion.

Assess The Automated Features And The Ability To Connect With Other Systems

The integration of database testing tools with your current workflow and automated QA capabilities stands as a vital requirement for modern, efficient software development processes. So, you should opt for database testing tools which integrate well with your CI/CD pipeline to run tests automatically with each code modification.

Find The Balance Between Cost And Functionality

The selection process requires a vital evaluation of tool expenses relative to their available features. The fundamental needs of free open-source tools are met, but paid solutions provide both advanced features and professional assistance, and superior performance.

Undoubtedly, your final choice should be based on the strengths of the product from the database testing tools list and how they meet your project’s specific needs. However, it is important to note that you need to carry out a pilot test on a small project (or use the free trial) before proceeding with a complete commitment.

The assessment should evaluate how simple the system is to deploy, how much area it covers, and how well your team accepts it. Only if the pilot is successful should you use the tool for a larger project.

The Role of AI in Modern Database Test Automation Tools

AI transforms DB testing through automated systems, which decreases the need for human manual work. The system generates test cases to check complex databases and produces authentic test data with multiple characteristics while maintaining data confidentiality. The streamlined method enables faster and smarter database verification, which results in higher reliability at a reduced cost. To sum up, AI in DB testing offers:

• Optimizing settings of the datastore for peak performance.
• Finding and fixing data inconsistencies automatically.
• Using data analysis to help design database schema elements, which results in optimal structural designs.
• Predicting upcoming problems that could lead to storage bottlenecks and query slowdowns, and hardware failures.
• Interacting with databases through natural language interfaces.

Bottom Line: Ready To Boost Your Database Quality with Database Testing Tools?

Database testing automation tools are essential for ensuring that your databases are working correctly and reliably. These database testing tools are crucial for automating tasks that would be difficult to do manually. Choosing the optimal tool among a variety of database testing tools depends on several factors, including:

• The type of database you’re using.
• Your project’s specific requirements.
• The kinds of tests you need to perform.
• The core functionality and features you are looking for.
• Affordable price for the tools, which suit your needs and budget.

Furthermore, the integration of AI into DB testing automates routine tasks, enhances dataset quality, removes inconsistencies, and provides advanced analytics. So the correct selection guarantees that you will get the appropriate functionality needed to perform effective quality assurance. Contact Testomat.io today to learn how our services can help you prepare a good test environment and resolve performance issues with database testing tools.

The post Best Database Testing Tools appeared first on testomat.io.

At first glance, Playwright appears to be a godsend for automation QA experts involved in browser test creation and execution. It is fast, excels at dynamic content handling, has a built-in test runner and test generator, and allows for seamless CI/CD integration.

That said, Playwright has a few shortcomings. It supports a limited number of programming languages, displays inadequate legacy browser support, doesn’t see eye to eye with some third-party tools (like test management solutions or reporting systems), is honed primarily for mobile browsers, presents significant problems in test maintenance (concerning test scripts and locators), and has a steep learning curve. Given such downsides, it makes sense to consider viable alternatives to Playwright.

This article offers a list of top Playwright alternatives, compares the pros and cons of various test automation frameworks, and gives tips on choosing the right Playwright alternative for particular use cases and testing needs.

The Top 12 Playwright Alternatives

Testomat.io

This is probably the best Playwright alternative we know of. Why? Because it is a multi-functional test automation tool that enables not only browser testing but in fact all types of QA procedures to boot (usability, portability, scalability, compatibility, performance testing, you name it). It allows for parallel or sequential cross-browser and mobile testing on multiple operating systems and mobile devices (both Android and iOS). The tool integrates with Playwright and its counterparts (for instance, WebDriverIO), CI/CD tools, and third-party apps (like Jira).

What sets Tesomat.io apart from its competitors is its outstanding test case, environment, and artifact management capabilities, as well as real-time analytics and reporting features. Plus, testers can involve non-tech employees in their workflow, enabling them to utilize BDD format and Gherkin syntax support when describing testing scenarios.

Although for novices in cloud-based test management systems, the learning curve may seem quite steep, the modern AI toolset offered by Testomat.io is an excellent alternative to Playwright MCP. What makes it especially attractive is the ability to choose between the basic free version and two commercial ones, with the Professional tier at $30 per month, which suits startups, small, and medium-size businesses perfectly.

Cypress

If you need to quickly test the front-end of web applications or single-page apps, Cypress is a good choice. It is easy to set up, offers automatic waiting for elements (which eliminates the necessity for manual sleep commands), has superb real-time debugging capabilities, and provides built-in support for stubbing and mocking API requests. Moreover, its cy.prompt and Cypress Copilot tools are AI-powered, enabling code generation from plain English descriptions.

On the flipside, you can write tests only in one language (JavaScript). Plus, tests don’t work in multiple browser sessions, and you have to install third-party plugins for XPath, reports, and other crucial features.

Cypress has both free and paid options (the latter are hosted in the cloud, not on the user’s hardware). The cheapest Team plan, allowing for 10,000 tests, costs $75 a month, and the priciest is the Enterprise plan with customized fees.

Selenium

It is an open-source test automation framework that is honed for cross-browser testing of enterprise-size solutions and mobile apps where extensive customization is mission-critical. It consists of three components (IDE, Grid, and WebDriver), which, unlike Cypress or Playwright, play well with a great range of popular programming languages. Plus, it allows for versatile integrations and parallel testing, enjoys extensive browser compatibility, enables headless browser automation, and boasts b community support.

Among the best Selenium’s fortes are Healenium and TestRigor. The first is an ML-driven self-healing test automation library that adapts to changes in web elements in real-time. The second is a cloud-based AI-fueled tool that enables the creation and maintenance of automated tests without any prior knowledge of coding.

Among Selenium’s disadvantages, one should mention the sluggishness of the script-based approach it employs, the need for third-party integrations (for instance, TestNG), expensive maintenance, and problematic test report generation.

CodeceptJS

The most appreciated advantages of this innovative, open-source testing platform are its simple BDD-style syntax, integration with modern front-end frameworks (Angular, Vue, React, and others) and CI/CD tools, high speed, and automated AI-driven creation of page objects with semantic locators, enabling the swift finding of elements in them. Thanks to its consistent APIs across a gamut of helpers, CodeceptJS users can easily switch between testing engines while interactive pauses for debugging and automatic retries remarkably streamline and facilitate the QA pipeline.

In a word, it is a cross-platform, driver-agnostic, and scenario-driven tool with AI-based features (such as self-healing of failing tests) that can be applied in multi-session checks (typically, functional and UI testing) of web and mobile solutions. The AI providers it integrates with encompass Anthropic, OpenAI, Azure OpenAI, and Mixtral (via Groq Cloud). What is more, the CodeceptJS configuration file allows users to configure other providers within the system. If you need consultations concerning the platform’s operation or devising test cases, you can obtain it on community forums or through GitHub issues.

Yet, its versatility and ease of use come with some downsides, namely the immaturity of AI features, less efficiency in handling complex web and native mobile apps, and limited support for certain cloud platforms.

Gauge

It is a lightweight, open-source framework primarily designed for user journey verification and acceptance testing of web apps. Gauge can perform browser automation when coupled with other tools. The pros of Gauge are its readable and foolproof Markdown test specifications, support for multiple programming languages, wide integration capabilities (including automation drivers, CI/CD tools, and version control solutions), and a ramified plugins ecosystem.

Gauge’s demerits are mostly the reverse side of its merits. While broad-range integration is a boon in itself, it spells excessive reliance on third-party drivers, each of which must be configured and managed directly. Likewise, the open-source nature of the tool means that the support typically comes from the community, which can fail to respond to requests in a heartbeat.

Jest

The in-built mocking capability of this Meta-launched framework enables easy cross-browser testing of separate modules, units, and functions within a solution. Besides, it is simple to set up, with its learning curve being rather mild. However, Jest’s free nature may cost you a pretty penny down the line with maintenance and server-related expenditures accumulating over time. Besides, some users claim that large amounts of code and high-capacity loads dramatically slow the system.

WebDriverIO

This is a great alternative to Playwright for QA teams that rely on CI/CD integration-heavy workflows and are looking for WebDriver-based automation. The framework allows testers to conduct cross-browser and mobile testing with high test coverage, thanks to its extensive plugin ecosystem, which offers enhanced automation capabilities. However, it has significant configuration requirements, lackluster reporting, limited language support (mostly JavaScript and TypeScript), and concurrency test execution limitations.

Testcafe

Unlike the previously mentioned tool, this one doesn’t need browser plugins or WebDriver to run the test, because TestCafe does it directly in real browsers. Its best use cases are those that require parallel test execution on real devices without additional dependencies. Yet, with TestCafe, you can write tests only in JavaScript or TypeScript, and you won’t be able to replicate some user interactions with the device (such as clicks and double clicks).

Keploy

It is free for owners of the Apache 2.0 license. Keploy’s key perk is its capability for automated stub and test generation, enabling QA teams to build test cases out of real-life user interactions. It saves testers time and effort they would otherwise spend on creating tests manually. Such a feature, in combination with numerous native integrations and AI-driven automation, allows experts to radically step up test coverage and suits perfectly for API and integration testing routines across various solutions.

Among cons, a steep learning curve and limited support for non-JavaScript-based applications are worth mentioning.
In addition to the mostly free frameworks mentioned above, let’s explore paid alternatives to Playwright with observability features.

Katalon

It is geared toward testing mobile, app, and desktop applications by both experts and non-tech users. Katalon’s user-friendly UI and AI utilization make it a solid tool for keyword-driven testing with fast scripting potential. Outside its specific hardware requirements, Katalon’s main drawback is the price. Its most basic version (Katalon Studio Enterprise) costs $208 a month, with each new functionality increasing the price. Thus, for the Katalon Runtime Engine, you have to fork out $166 a month more, and for Katalon TestCloud – plus $192.

Testim

It is praised for codeless test recording, easy scalability, reusable test steps and groups, drag-and-drop visual test editor, extensive documentation, constantly available customer support, and plenty of AI-driven features (smart locators, help assistant, self-healing capability, and more). The major downside of Testim is the vendor’s obscure pricing policy. They customize plans to align with test coverage and needs, and extend numerous enterprise offerings (Testim Web, Mobile, Copilot, etc.), the price tag of which is declared on demand.

Applitools

Efficiency, speed, seamless integrations with other testing frameworks, advanced collaboration and reporting opportunities, generative test creation, and AI-fueled visual testing are the weighty assets the platform can boast. However, it is rather hard for novices to embrace, subpar in customization, and provides limited manual testing support. You could put up with these shortcomings but for Applitools’ price. Its Starter plan is $969 a month (to say nothing of the custom-priced Enterprise tier), which makes Applitools an upmarket product hardly affordable for small and even medium-size organizations.

Let’s summarize the information about Playwright alternatives.

Top 12 Playwright Alternatives Contrasted

A detailed comparison is more illustrative when presented in a table format.

As you see, there are plenty of browser testing frameworks, which means that selecting among them is a tall order. Perhaps it is better to stay with the classical Playwright?

Reasons to Choose an Alternative over Playwright

To make a wise and informed decision concerning the choice of a Playwright alternative, you should consider project needs that make Playwright a misfit. Opting for another framework makes sense if:

1. You face specific requirements. The need for better mobile testing capabilities or extensive support for legacy systems calls for something other than Playwright.
2. You look for a milder learning curve. Setup and debugging in TestCafe or Cypress are more intuitive and simple to master for greenhorns in the field.
3. Testing speed matters. Some alternatives (like Cypress) enable faster testing than Playwright does.
4. You lack expertise. Testim and Selenium are no-code frameworks accessible to non-tech users.
5. Multiple third-party integrations are vital. Many tools (CodeceptJS, Gauge, Keploy, WebDriverIO, etc.) offer wider integration options and/or a versatile plugin ecosystem.
6. Constant support is non-negotiable. Users of open-source platforms like Playwright can rely only on peer advice and recommendations. Professional 24/7 technical support is provided exclusively by commercial products.

Conclusion

Playwright is a high-end tool employed for automating browser testing across different platforms and browsers. However, other tools can surpass it in terms of the range of programming languages, legacy browser support, simplicity of use, no-code options, and meeting specific project requirements. Ideally, you should opt for a framework that enables comprehensive cross-browser and cross-platform testing, plays well with multiple third-party systems, provides real-time reporting and analytics capabilities, and is free (or at least moderately priced). Testomat.io is an optimal product that ticks all these boxes.

The post Playwright Alternatives: Top 12 Tools for Browser Automation & Testing appeared first on testomat.io.

To address this challenge, you can use Selenium WebDriver’s locators to find and interact with web elements. While also basic element locators like ID, Name, Class Name, and CSS Selectors often work well, they are insufficient when elements lack unique attributes or their properties change frequently. That’s when you can use XPath to navigate a web page’s complex structure to find specific elements. In this article, we will discover what  XPath in Selenium is, explore the different types of XPath, reveal basic and advanced techniques, and learn how to write XPath in Selenium.

What is Selenium?

Being an open-source suite of tools and libraries, Selenium enables teams to make the testing of website functionality automated. With its cross-browser, cross-language, and cross-platform capabilities, they can test across different environments.

Selenium supports  Java, JavaScript, C#, PHP, Python, and Ruby programming languages, which allows teams to integrate it with existing development workflows.

Furthermore, it also offers extensive browser compatibility with major web browsers like Chrome, Firefox, Safari, Edge, and Opera to cover all major browsers, while being flexible in terms of its ability to be compatible with different automation testing frameworks like TestNG, JUnit, MSTest, Pytest, WebdriverIO,

Selenium Primary Components

• Selenium WebDriver. It is a programming interface which can be used to create test cases and test across all the major programming languages, browsers, and operating systems. Regarding the cons, it has neither built-in test reporting nor a centralized way to maintain objects or elements.
• Selenium Grid. It is a smart proxy server which allows automation testers to run tests on different machines against different browsers.
• Selenium IDE. It is an easy-to-use browser extension which records your interactions with websites and helps you generate and maintain site automation, tests.

What is XPath in Selenium?

XPath, which is known as an acronym for XML Path Language, is a query language used to uniquely identify or address parts of an XML or HTML document. Generally, you can use it to do the following:

• To query or transform XML documents
• To move elements, attributes, and text through an XML document
• To look for certain elements or attributes with matching patterns
• To uniquely identify or address parts of an XML document
• To extract information from any part of an XML document
• To test the addressed nodes within a document to determine whether they match a pattern

When to use XPath

• When elements do not have unique IDs, names, or class names
• When elements are dynamic or change quickly
• When there is a need to locate elements based on their text content or position, which is relative to other elements

Overview of Basic XPath syntax in Selenium

• // – it indicates the current node
• tagname (e.g., div, input, a) – it indicates the tag name of the current node
• @attribute (e.g., @id, @name, @class) – it indicates the attribute of the node
• value (e.g., //input[@id='username']) – it indicates the value of the chosen attribute

The Difference Between Static | Dynamic XPath in Selenium

Before we start considering XPath types, it is essential to define “static” and “dynamic” XPath in the context of web elements. It needs to be done because it will determine the choice and robustness of your XPath and will result in effective test automation:

Static XPath. It is a direct and absolute path, which is specified from the root of the webpage to point to an element’s location in the Document Object Model (DOM) hierarchy. But any change in the UI can break the path. Here is XPath in Selenium example:

/html/body/div[1]/div[2]/input

This path starts from the root and traverses down to the desired element.

Dynamic XPath. It is a relative path that uses flexible criteria to locate dynamic web elements whose attributes or positions change frequently on a webpage. In contrast to Static XPath, the elements in the dynamic  XPath are more resilient to changes in the UI. To create dynamic XPathes, you can use the following:

• contains(), text(), starts-with()dynamic element indexes
• logical operators OR & AND separately or together
• axes methods

Here is xpath examples in selenium:

//input[contains(@id, 'user')]

This expression selects any <input> element with an id attribute containing the substring ‘user’.

Sum up: Static VS Dynamic XPath

Static XPath (typically absolute XPath) provides a full path from the HTML root to an element, making it very prone to failure in terms of breaking with any minor change in the page’s HTML structure.

Dynamic XPath locates elements whose properties/positions change frequently to guarantee that test scripts are less prone to failure in the face of UI updates or dynamic content. With dynamic XPath techniques, you can create stable locators, which remain stable despite UI changes, to drastically cut down on test automation maintenance, while you may face frequent test failures by relying on static XPaths in dynamic web applications.

What is an XPath locator?

XPath locator in Selenium WebDriver is a technique used in automation testing to identify web elements and help automation tools like Selenium interact with them even in complex or dynamic DOM structures. They support both absolute and relative paths, providing adaptable element identification via relationships, attributes, or text.

Types of XPath in Selenium

You can use two ways to locate an element in XPath – Absolute XPath and Relative XPath. Let’s review them with some XPath examples in Selenium below:

Absolute XPath

It contains the location of all elements from the root node (HTML), where the path starts, and specifies every node in the hierarchy. However, the whole XPath will fail to find the element if there is any change/adjustment of any node or tag along the defined XPath expression. The syntax begins with a single slash, “/”, and looks like this:

/html/body/div[1]/div[2]/form/input[2]

We see that if any new element is added before the target element, or if the structure of the divs, form, or inputs changes, this XPath will fail and break your test automation script.

Relative XPath

As the most commonly used and recommended type, it tells XPath to search for the element anywhere in the document. Starting with a double forward slash “//”, it begins from the middle of the HTML DOM structure without the need to initiate the path from the root element (node). The syntax looks like this:

//input[@id='username'] or //button[text()='Submit']

How To Create XPath in Selenium

When writing XPath in Selenium, you can do it by applying various types of XPath locators. Let’s consider them:

• Using Basic Attributes
• Using Functions
• Using Axes

Using Basic Attributes

Using XPath Functions in Selenium

XPath’s functions are used to determine elements by their attributes, positions, and other factors.

Using XPath axes in Selenium

With Axis, you can see the relationship to the current node and locate the relative nodes concerning the tree’s current node. So, the XPath Axis uses the relation between several nodes to find those nodes in the DOM structure:

Below you can find commonly used XPath axes:

We would like to mention that you can apply chained XPath in Selenium concept, where you can utilize multiple XPaths in conjunction to locate an element that might not be uniquely identifiable by a single XPath expression. In other words, instead of writing one absolute XPath, you can separate it into multiple relative XPaths. When chaining XPaths, you can improve the accuracy and robustness of the element location strategy, thus making the automation scripts more stable.

How to Use XPath in Selenium: Practical Examples

Example 1: Locating an Element by ID

The simplest way to locate elements using XPath is by their unique identifier, which is, as a rule, the id attribute. It looks like this:

WebElement element = driver.findElement(By.xpath("//input[@id='username']"))

In this example, you can use the <input> element where the id is “username.” With the findElement method, you can return the element for further interaction, checking its presence, or entering data.

Example 2: Traversing Using Axes

In this example, we consider an advanced technique to traverse the DOM’s structure based on how elements relate to each other.

WebElement parentelement = driver.findElement(By.xpath("//span[@class='label']/parent::div"))

We can see that the parent axis is applied to find the parent <div> element of a <span> with the class “label”. When an element, which you’re aiming to locate, has no unique identifying attributes, but can be found by its relationship to parent or sibling elements, XPath’s axes can be useful to achieve this goal.

html
└── body
    └── div#form
        ├── label        (Username or Email)
        ├── input        (name="log")
        ├── label        (Password)
        ├── div          (class="wp-pwd")
        ├── input        (name="rememberme")
        ├── label        (Remember Me)   
        └── button       (Log in)

Result of copying XPath with Developers’ tools are the next:

Example 3: Copy Xpath

//*[@id="user_pass"] //Copy XPath

Example 4: Copy full XPath

/html/body/div[1]/form/div/div/input.

What Are the Advantages of XPath Locators?

• With XPath, complex searches are becoming more flexible to allow you to locate items using a wide range of parameters.
• When you work with web pages with dynamic content, XPath can easily adapt to changes in page structure.
• It can traverse the DOM in both directions, which means moving from parents to children or from children to parents and siblings, to target elements that are structurally related to a known and stable element.

What Are the Disadvantages of XPath Locators?

• Complex XPath queries may be more slowly compared to simpler locators like CSS selectors.
• When relying on specific structures or attributes, XPath expressions may fail if the page structure changes.

Best Practices for Using XPath in Selenium

Here are some of the tips to follow when using XPath in Selenium:

• You need to use relative XPath to write more adaptable and maintainable locators compared to absolute XPath, which is based on the complete path from the root node.
• You need to keep XPaths as short and specific as possible to make them easier to maintain and improve.
• You need to apply functions like contains(), starts-with(), and text() if there is a need to create XPath expressions for processing dynamic elements with changing attributes. The contains() function is suitable when attributes such as id or class have variable values.
• When direct attributes aren’t enough, you can opt for XPath axes to locate elements through their relative position to a stable and identified element.
• Before incorporating an XPath into your code, you should test it directly in the browser’s console to make certain it works correctly.

Topics interesting for you:

• Playwright VS Selenium VS Cypress: A Detailed Comparison
• Playwright vs Selenium: Will Selenium Rise Again?
• Tutorial Selenium Exceptions & mastering to handle them
• Journey: Migrating from Selenium to Playwright

Bottom Line: Ready to use XPath in Selenium?

Applying XPath in Selenium while conducting the automated testing process is useful and effective for your teams. Whether they use a simple XPath or a more complex one, choosing the right XPath is crucial for test case stability. Being a powerful tool, it provides a flexible way to build robust Selenium test scripts that can handle a variety of web page structures with dynamic content and make sure they won’t fail if any of these locators change later.

Drop us a line if you want to learn more additional information about XPath in Selenium, and the testomat.io team is glad to provide software test automation services

The post XPath in Selenium appeared first on testomat.io.

— Why is this approach to development so in demand among various teams?
— What makes it unique?

Let’s break it down further

What is Application Lifecycle Management?

ALM is an approach to the development process that covers all its stages, from initial planning to the final deployment of the application. However, saying that ALM is simply an approach to application development is not enough. It is a whole philosophy embedded in Agile methodologies, which allows teams to create high-quality digital solutions.

It’s common to equate ALM with software development. Yet this simple approach is too limiting; ALM is much more than this. In fact, an application’s lifecycle includes the entire time during which an organization is spending money on this asset, from the initial idea to the end of the application’s life

David Chappell,
the Principal of Chappell & Associates

Note. ALM should not be equated with Product Lifecycle Management (PLM). What is the difference? The former focuses solely on the lifecycle of an application, while PLM is a more comprehensive concept that covers not only software development but also other aspects of the product lifecycle, such as hardware, manufacturing, product marketing, etc.

Watch the expert’s video to learn more: What is Application Lifecycle Management (ALM)?

Difference Between ALM VS SDLC

Another common misconception is the software development lifecycle (SDLC). Let’s explore why:

What conclusion can be drawn from this? SDLC is a narrower concept that pertains exclusively to the creation of a digital solution. It can be considered a part of ALM.

Phases of Application Lifecycle Management

— What do you think the entire lifecycle of the application begins with?
— Right, With the idea
— So, Why not create something that helps achieve a specific goal? The phased work on creating a digital solution starts from its inception.

As seen in the table above, in most cases, ALM includes five phases. Let’s talk about them in more detail…

#1: Requirements Specification

At the first stage, it is necessary to economically justify the idea. That is, to answer the question: what do we want to achieve from this application? All stakeholders should participate in the discussion. Their expectations form the basis for the future product requirements.

Next, it is important to clearly formulate these requirements.

Often, the requirements specification has a hierarchical tree structure. That is, each requirement has its own sub-requirements.

However, teams that have implemented application life cycle management tend to prefer another approach. For example, it may be more convenient to:

• categorize the requirements (by specific functionality, module, etc.);
• structure them by priority (what should be implemented first, what can be done later).

This approach is more in line with Agile methodologies.

#2: Development Phase

Once the requirements are gathered, development of the software application can begin. At this stage, a distinctive feature of ALM will be total collaboration – different teams participate in the application development.

For example, ALM Processes participant’s responsibilities:

→ Project Manager handles financial matters, estimating the duration and cost of the project.
→ Development team members formulate key tasks for design and coding.
→ Testing team determines the scope of work for quality verification, preparing test cases.
→ Marketers and Customer Representatives ensure that the product meets the target audience’s requirements.

Also, before development begins, planning for the sequence of releasing new features is carried out.

#3: ALM testing

Testing and development of the software product go hand in hand in ALM. Testers check the quality of the solution after the release of each new feature or update. Any detected errors are immediately sent to the development team for resolution, after which the QA specialist runs the tests again.

To speed up and increase the accuracy of this process, it is advisable to use ALM testing software.

#4: Deployment

This stage involves releasing the digital solution to end users. How this will be implemented depends on the type of software product.

For instance, it could be a SaaS solution. It needs to be deployed on internal servers. Local applications require deployment, installation, and configuration on the client’s server. Mobile and hybrid products are distributed through app stores. There are other deployment methods as well, but we will not go into detail about them in this material.

#5: Maintenance of the Software Product

At this stage, the team faces one main task — ensure that the application meets user expectations after release! This involves performing the following actions:

• Timely detection of errors and their quick resolution.
• Planning future updates to improve the digital solution.
• Implementing new functionality based on user feedback.
• Discontinuing the use of the product (or its current version) and releasing another application/version using new relevant technologies.

The last activity is called the Retirement of the Product. It is a vital part of ALM.

Benefits of ALM

Thus, why implement ALM if you can continue working according to the familiar SDLC? If you’ve had this question, let us quickly present the key benefits of application lifecycle management.

Improved Communication on the Project

Mismatch between the final product and business requirements, missed deadlines, low team morale, etc. Admit it, you’ve probably faced these issues on projects at least once in your life. One of the reasons for each of them could be poor communication within the team and beyond.

Well, ALM helps prevent such difficulties and their consequences. Each application lifecycle management tool serves as a single platform for collaboration between developers, quality assurance engineers, project managers, and relevant stakeholders. Anyone involved with the product can participate in its creation: track progress, monitor project status, make suggestions, and more.

This helps prevent misunderstandings and contributes to achieving project goals.

Effective Project Planning

Proper planning of the software product’s work is the key to successful development. Clear project plans allow:

• proper prioritization
• accurate project time estimates
• effective resource allocation
• avoiding cost overruns
• tracking progress
• timely identification of issues
• promptly addressing emerging problems.

In other words, ALM allows teams to create a clear project roadmap, which they will then follow to maintain the correct development course.

Reliable ALM Software Testing

As you remember, quality assurance is an integral part of application lifecycle management. Moreover, QA processes in this approach are characterized by clarity and structure. This is facilitated by the use of ALM testing tools. These platforms allow test case creation, have features for managing test data and support test automation.

Risk Minimization

Is the development of the application associated with risks? The answer is clear. Yes. These risks can be caused by technical debt, poor requirements gathering, changes in business priorities, and more. All these risks can be effectively managed using an application lifecycle management tool. Typically, such platforms have enough functionality to identify, assess, and mitigate risks.

Continuous Process Improvement

Specialized tools allow the collection and analysis of many metrics. For example, users have access to project documentation, detailed test reports, and development time data. By tracking various indicators, team members can promptly identify issues with source code and other bottlenecks in the project. What’s the result? Increased team efficiency and improved the quality of the final product.

ALM in Software Testing

The role of ALM in testing is hard to overestimate. This approach ensures smooth integration of QA efforts into the broader lifecycle of an application. In other words, it guarantees that tests will be executed at each development stage.

The following components of ALM for testing are highlighted:

• Requirements Management. Application Life Management helps clearly define requirements and ensure their precise alignment with test plans. Common practice is linking requirements in Jira with the ALM test management tool to track their traceability.
• Test Planning. This activity involves developing a clear testing strategy and its objectives. The teams will need to determine which types of testing to use and create test plans. These plans are easy to document and track in an ALM tool for testing.
• Test Design. It’s time to write test cases and test scripts. Ready? Now you can check whether the application works properly.

• Test Execution. Managing test runs, monitoring their execution, and analyzing results — all of this is available in the ALM test management tool.

• Defect Management. Did you find a defect during testing? ALM implies documenting it, determining its priority, and providing recommendations for resolution.

• Test Automation. ALM supports the use of automated testing tools. This speeds up the process and improves the accuracy of quality assurance results.

• Continuous Testing. ALM perfectly aligns with Agile development principles. This approach fosters continuous testing throughout the development process.

• Reporting and Metrics. Detailed reports help teams identify areas for improvement by studying various metrics. These include test coverage, test execution status, defect trends, and more.

• Release Management. ALM QA testing ensures that all tests (including acceptance tests) have been conducted and that the digital product is ready for market release. Post-release testing is also possible. This allows tracking potential issues.

What is ALM Testing Tool?

An ALM testing tool is a platform that allows optimizing QA processes on a project. These tools help plan, track, and execute tests, as well as provide advanced test Reports and Analytics

In other words, it is a unified platform that combines all the functions necessary for testing teams: requirements management, test case creation, defect tracking, and reporting.

The Role of ALM Test Management Tool in Development

The tools used in application lifecycle management offer users comprehensive functionality. They help optimize all activities on the project.

Key aspects of development in which ALM platforms are indispensable:

• Requirements management. As you can see, this process is important not only for creating relevant test cases. It is crucial for the entire development process.

• Planning and scheduling. Detailed project planning is half the success. ALM tools allow you to create project plans, develop a schedule for implementing tasks, and allocate resources effectively.

• Version control. These tools easily integrate with version control systems. This is very convenient because you can revert to the desired version of the source code at any development stage.

• Test management. We’ve already discussed the role of ALM in testing. So, we will just remind you — the AML test management tool helps optimize test planning and execution. This guarantees high-quality final products.

• CI\CD practices. Tools allow you to automate the process of building, testing, and deploying code. This leads to faster development, early detection of defects, and reduced risk of errors during software delivery to end-users.

What else to expect from ALM tools? They help manage the release of digital products, ensure regulatory compliance, and are used for monitoring after deployment.

ALM Tools: What to Consider When Choosing?

If you need to choose an AML test management tool, ask yourself the following questions

• What is the size of my team? Larger or remote teams may require tools focused on collaboration, detailed reporting, and good scalability.

• What kind of project are we working on? Choose a tool based on your development methodology. For Scrum or Kanban workflows, you will need a more flexible platform. For a waterfall project, traditional planning and reporting will be more suitable.

• What infrastructure does the ALM tool need to integrate with? Assess whether the tool can be integrated with the CI\CD pipeline, version tracking system, and other services already used in the project.

• How much time will training and adaptation take? An AML test management tool with a steep learning curve is not the best choice, as it may slow down development.

• What features does the tool offer? Remember that ALM is a comprehensive approach covering all aspects of the application life cycle. Make sure the tool has sufficient capabilities, especially in areas such as test automation.

• Does the tool provide built-in reporting and analytics? Detailed test reports and data analytics are essential for informed decision-making. If they are not available, you may have to rely on third-party services, which is inconvenient.

• What if problems arise? Prioritize tools with a well-developed community and comprehensive documentation.

Next, we present a list of the best ALM tools that best meet the criteria mentioned above.

Top 5 ALM Testing Tools

We have selected five tools whose capabilities allow teams to effectively implement application lifecycle management in their projects.

Testomatio

The testomat.io test management system helps teams optimize their testing efforts throughout the entire lifecycle of an application. The platform bridges the gap between manual and automated testing, allowing users to run various types of tests in one place. It offers the following key features:

 Testomatio ALM test management tool key features:

• Test Case Management. The tool supports both manual and automated test cases, enabling users to create, organize, and manage them efficiently.

• Test Automation Integration. With just one click, manual tests can be converted into automated ones. Additionally, the TMS allows importing test cases from other testing tools.

• Defect Management. The system enables defect tracking from detection to resolution. Users can filter, cluster, and monitor defect severity for individual test runs or the entire project.

• Reporting and Analytics. Need to track testing progress? Use real time reports. Looking for insights after test execution? Comprehensive reporting is available. Users can also leverage live documentation and an analytics dashboard to monitor key metrics.

• CI\CD Support. Testomat.io seamlessly integrates with popular CI\CD tools like GitLab or GitHub, CircleCI, Jenkins and AzureDevOps enabling continuous test execution, real-time monitoring, and defect notifications.

• Collaboration. Human-readable reports, BDD support, data visualization, and integrations with tools like Slack enhance team collaboration on projects.

Jira

Jira is a highly demanded project management and bug tracking system among IT teams. It is most commonly used by development teams for working with requirements, defect management, and task execution control. And although the system does not offer a test automation function, this issue can be easily resolved by integrating it with a test management tool — for example, with Testomat.io.

Jira key features, as alm tool in testing:

• Test Case Management. Thanks to seamless integration with Testomat.io, users can easily manage test cases using a familiar tool. All they need to do is link them to user stories and Jira issues.

• Test Execution. Jira supports the management and tracking of manual tests. Automation is only available through third-party integrations.

  And many ALM test management tool providers give this opportunity.

• Defect Tracking. The platform allows users to track defects and integrate them with test cases. This enables control over the development process throughout the application life cycle.

• Agile Integration. As one of the most popular project management tools, Jira supports Agile development methodologies (Scrum, Kanban). Teams can link testing with sprints or user stories.

• Reporting and Traceability. The tool provides users with built-in reports that help track test coverage, test execution, and defect status.

Microsoft Azure DevOps

Microsoft Azure DevOps, formerly known as Visual Studio Team Services, is a set of tools for development teams that have implemented DevOps practices in their project. It allows managing source code, supports continuous integration, QA, and is used for release management.

Azure DevOps key features:

• Agile Project Management. Supports Scrum and Kanban for planning and tracking workflows.

• Test Management. The platform holds a top position as an ALM test management tool within the QA community worldwide. It allows managing manual tests, with the possibility of integration with testing tools for test automation.

• CI\CD Support. Automates pipelines for building, testing, and releasing software.

• Version Control. Enables code repository management through integration with version control systems.

• Defect Tracking and Reporting. The tool provides users with access to monitoring dashboards. With their help, you can track key project metrics and identify defects.

IBM Rational Team Concert

IBM Rational Team Concert (RTC) is a solution for collaborative management of an application’s lifecycle. The functionality of the tool meets the needs of teams at all stages, from planning to post-release support.

Key features RTC AML testing software:

• Collaboration. The platform offers users discussion boards and team rooms for collaboration. Notifications about changes or new comments also play an important role.

• Version Control. You can use the built-in version control system or integrate with an external service.

• Source Code Management. Users have access to the Code Reviews feature and integration with other IBM tools for source code analysis.

• Quality Management. Interested in comprehensive test management? Consider RTC as a tool for your project. However, it goes beyond a traditional namely ALM test management tool. With it, you can plan and execute tests, as well as track the progress and results of testing.

Tricentis

The Tricentis platform is a comprehensive solution for continuous automated testing. It is often used by teams due to its versatility. Here, different types of tests can be run, test cases can be managed, and results can be analyzed.

• Continuous Testing. The tool supports agile methodologies and DevOps. What does this mean in the context of QA? You can test individual functions or the entire application throughout the entire development cycle.

• Test Automation. To accelerate QA, you can automate your tests with Tricentis. Importantly, these can be any type of tests, such as functional, regression, and performance testing.

• Test Management. With the right AML test management tool, you will have complete control over QA processes. What can not say about Qtest from Tricentis. Plan and executing tests, identifying defects, tracking compliance with requirements, and generating detailed reports are quite curly with the Tricentis testing tools, admit their customers.

• Collaboration. Tricentis easily integrates with third-party services (such as Jira or Jenkins). As a result, testers gain access to real-time analytics, dashboards, and other collaborative features.

• Risk-based Testing. This approach to testing is fully justified. It allows you to prioritize test cases and focus on the most critical areas of the application.

You’ll find this valuable:

Comprehensive comparison of Qtest & Testomat.io test management system

Conclusion

ALM is an approach to creating digital products that covers far more aspects than the classic SDLC. Use it for clear planning, automation, and tracking of the entire lifecycle of an application, including testing.

Investing in a more advanced ALM test management tool is sensible if the company is on the way to a large-scale enterprise project and plans its long-term development with ALM as a single source of truth for the entire team, following the best practices of Quality Assurance.

The post What is ALM Test Management Tool, its Benefits | Top 5 of the Best appeared first on testomat.io.

What is Codeless Testing?

Codeless testing refers to test automation without requiring extensive programming knowledge. These tools use intuitive interfaces, record-and-playback mechanisms, AI-driven testing, and visual workflows to enable QAs to create, execute, and maintain tests with minimal coding effort.

What Ideas Codeless Testing Brings to Modern QA Practices

Codeless platforms show us a big change in how we do Quality Assurance (QA) today. Using them is a natural step forward in software development. These tools come with simple interfaces and a window for recording ready-made prompts. A central focus is to be efficient, intuitive and easy to use. Not only that, for instance, the best codeless automation tool may give QA teams the following strong edge:

Bridging the Gap Between Technical and Non-Technical Testers

In the past, testers without much coding experience could not fully engage in automated testing. Low code codeless testing changes that. Now, people with different skills may take part in testing. Sometimes, they are not QA, Business Analysts (BA) or Project Managers (PM) with specific knowledge about their field can easily create and execute test cases on CI\CD.

Find out more:

What are the Three amigos in Agile?

This allows the development team to be on the same page. Also, it brings teamwork a different ideas of how to improve it. In turn, it has a positive impact, together team efforts facilitate delivering high-quality software.

Fostering Collaboration Across Development and QA Teams

Codeless testing tools help both tech-savvy and non-tech testers in QA teams. They offer a visual environment. Many modern codeless tools provide a shared space, which means everyone can see what’s happening throughout the software development process (SDLC). Both technical and non-technical team members can access test cases easily, track progress, and solve issues together. It helps development and QA teams remove traditional barriers of total misunderstanding, allowing for smoother communication and understanding. Openness makes everyone feel responsible for quality and leads to better software.

Additionally, codeless testing tools are easy to use for starter team members. This makes it easier for them to join the team and work on a board. A reduced learning curve leads to a more involved and productive team right away. As a result, QA teams become more flexible and effective!

Top Benefits of Codeless Testing Tools

Codeless testing tools offer several key benefits that make test automation more accessible and efficient. Here are the top advantages:

• Accessibility for non-technical users. Enables manual testers, business analysts, and other non-developers to contribute to test automation without coding skills.
• Faster test creation & improved software quality. Codeless tools reduce the need for special coding skills, so more testers can use them by generating new test cases and start automating existing ones quickly using recorders or drag-and-drop interfaces.
• Enhance efficiency and productivity. It reduces the need for manual work and even lowers the chance of making mistakes. Testers can then concentrate on other more important tasks, like analyzing test results and understanding results. Many AI-powered tools maintain automatic script updates when the application changes, reducing flaky tests, and certain codeless platforms allow parallel testing.
• Increased test coverage. Broader test coverage since more team members can create and execute tests, covering UI, API, and end-to-end scenarios.
• Integration with DevOps & CI\CD Pipelines. Aligns with modern development methodologies by enabling seamless integrations with popular tools like Jenkins, GitHub Actions, and cloud-based CI\CD systems, enabling automated testing as part of the deployment process.
• Faster feedback. With this smooth process development teams get feedback faster and solve problems quickly. It speeds the delivery of releases with improved software quality.
• AI testing modules. Many codeless tools leverage AI to self-heal test scripts, identify flakiness, and generate optimized test scenarios.

Overall, codeless automation helps teams get more done in less time while still keeping the quality of their testing high. This increased efficiency is very important today because getting products to market quickly can give teams a big leap over the competition. Apart we decided to distinguish:

Reduction in Time to Market for Software Products

Keeping a quick pace is key in today’s tough market. Codeless testing is important for speeding up the time it takes to bring software products to market as businesses develop software faster. It is significant because they need to meet changing market needs and provide value to their customers in time.

Secondly, traditional testing methods take a long time and use many resources. They can slow down the development process. On the other hand, codeless testing makes things simpler. It offers quicker cycles and keeps good accuracy.

As companies start using Agile and DevOps methods, codeless testing fits in easily. This efficiency effect that businesses finish development and deploy their products more quickly. By getting rid of the hard coding steps, codeless testing lets businesses innovate fast and get their products to market quicker.

Comprehensive Overview of Leading Codeless Testing Tools

The world of codeless testing is always changing. New tools appear with better features and functions. These tools meet many testing needs, whether for web and mobile applications, API, or desktop testing. It is important to know the unique strengths of each tool. This way, you can pick the one that best fits your organization’s needs.

Looking closely at the top codeless testing tools and their key features helps users make smart choices. It will empower them to improve their testing process.

Selenium IDE

Selenium IDE stands for Integrated Development Environment. It is a well-liked tool for codeless automation testing, especially for testing web applications. It is simple to use. Users can record, play back, and automate browser actions without needing to write any code.

A key feature of Selenium IDE is its easy record-and-playback system. Testers can use web applications as they normally would. The IDE captures these actions and creates test scripts automatically. Selenium IDE also provides many commands and assertions. This gives testers flexibility and control over their test cases.

In short, Selenium IDE helps more people do web application testing because it does not require complex coding. Its friendly interface, along with strong features, makes it a great tool for both newcomers and experienced testers who want to make their web testing easier.

Katalon Studio

Katalon Studio is a codeless testing platform that makes it easy to perform code testing on many platforms like web, API, mobile, and desktop. It is user-friendly and has advanced features. This makes it great for both newcomers and skilled testers, giving it a wide range of use.

A major strength of Katalon Studio is its complete API testing abilities. Users can send requests, check responses, and test APIs using different protocols like REST, SOAP, and GraphQL. This feature is important for making sure APIs work well. APIs are very important for today’s software applications.

Katalon Studio also offers many built-in keywords to help create test cases. This simplifies the testing process. For users with more experience, the scripting mode lets them write custom code to add more options. This mix makes Katalon Studio a good fit for teams with different skills.

TestCraft

TestCraft is a new codeless testing platform. It uses artificial intelligence (AI) to make test creation and maintenance easier. This helps make the testing process more efficient and better suited for today’s software development. TestCraft is different from other tools because it has AI features that improve how testing gets done.

The core of TestCraft is its AI test creation engine that leverages natural language processing. Users do not need to write test scripts by hand. Instead, they can create test scenarios with a simple drag-and-drop interface. The AI then looks at these scenarios and builds strong test scripts that can run on different browsers and devices.

TestCraft’s AI also helps with maintaining tests. Traditionally, this step can take a lot of time and effort. But TestCraft’s AI learns from each test run. It can automatically spot changes in the application and fix them, reducing the need for manual updates.

Leapwork

Leapwork is a popular platform for codeless automation testing. It uses a visual language to create and run tests. This makes testing easier for a wider range of people. Business users and domain experts can use it, even if they know little about coding.

The flowchart system in Leapwork helps users build test cases. They can drag and drop building blocks to show different actions, checks, and logic. This visual way of showing test cases makes it clear and simple to understand. It also helps in making and keeping complex test scenarios.

Leapwork has many ready-made building blocks for common actions and connections. This allows users to automate testing for different applications like web apps, desktop, and virtual environments. This flexibility makes Leapwork a strong choice for various testing needs across many fields.

Tricentis Tosca Testsuite

Tricentis Tosca Testsuite is a complete testing platform known for its risk-based testing method. It uses minimal coding, which helps teams focus on the parts of their applications that are most at risk. This way, they work more efficiently and make sure important features are tested thoroughly.

Tosca Testsuite uses model-based testing to keep coding to a minimum. Instead of creating traditional test scripts, users develop visual models showing how the application works and how users interact with it. These models help to create automated test cases, which cuts down the need for manual scripting a lot.

Also, Tosca Testsuite connects well with popular testing tools and frameworks, like Selenium and Appium, which enhances modern software development practices. This lets users manage existing testing assets and improve their overall testing capabilities across various test automation projects. This integration makes it easy to add Tosca Testsuite to different testing environments.

Ranorex Studio

Ranorex Studio is a complete test automation tool. It has a simple interface that works well for both beginners and experts. The easy-to-use design and many features make it a good choice for testing web, desktop, and mobile applications.

One key part of Ranorex Studio is its recording and playback function. This lets users create automated tests. You just need to interact with the application you are testing. It then captures your actions and makes test scripts automatically. It also has a strong object repository. This makes test maintenance easier by keeping UI elements and their details in one place.

In addition, Ranorex Studio gives different ways to test. It supports keyword-driven testing, data-driven testing, and a scripting option for advanced needs. This flexibility helps Ranorex Studio meet different testing needs and abilities in a team.

OpenText ex. UFT One (Formerly QTP)

OpenText, which was known as QTP, is a strong tool for testing without the need for code. This tool changes how tests are automated. It offers an easy-to-use interface for creating and running tests without needing much coding knowledge. With UFT One, QA teams can make their testing process smoother, improve software quality, and boost their testing skills. Its simple features, like drag-and-drop functionality and visual testing, make it a great choice for testers at any level. By allowing automation testing with little coding skills needed, UFT One increases efficiency and productivity in software testing projects.

ACCELQ

ACCELQ is truly a no-code, AI-powered, codeless test automation tool designed to streamline quality assurance across various applications, including web, mobile, API, desktop, and backend systems. It is a cloud-based solution, ACCELQ enhances collaboration and accelerates release cycles while reducing maintenance efforts. Integrates seamlessly with CI\CD pipelines.

Impact of Codeless Testing on Agile & DevOps Environments

Testing needs to be efficient, flexible, and able to fit into current workflows. Agile and DevOps methods have quick development cycles and aim for continuous integration and delivery. Codeless testing tools work well here.

Codeless testing makes it simpler to automate test cases. It allows tests to be run often and quickly. This quick pace is essential in CI\CD environments, where code changes happen all the time. Tests can start running automatically whenever new code is added. This gives developers fast feedback on how their changes affect the software. It is very important on every stage of the CI pipeline.

Usually, codeless solutions have easy-to-use interfaces. This allows more people involve in testing, which builds teamwork between developers, testers, and business side. Codeless testing is so simple enough, everybody can try it. Stakeholders can easily understand and even create test cases based on what users need and what the business wants. It is a core of Alile principles. As we note before anybody in team is respnsive for quality.

This teamwork leads to faster feedback loops. Fast feedback is key for Agile and DevOps methods, supporting software development step by step. Codeless testing tools make running tests faster. This leads to more test cycles and quicker feedback on the app being built.

Quick feedback helps QA teams find and Dev teams fix problems early in the development cycle. Fixing issues early costs less time and money. This way of working lessens the chance of big problems later on. As a result, teams can release software quicker and make sure it is better in quality.

Enhancing Feedback Loops and Iterative Development

Thanks to shorter feedback loops, developers get fast responses on their code changes. This quickness helps them fix issues right away. They can also try out new features with confidence and update the software based on real insights from testing.

Codeless VS Traditional Coding Testing Tools

The choice between codeless testing tools and traditional coding tools depends on a few important factors. These include what the project needs, the skills of the team, the budget, and the long-term testing goals. Knowing the differences between these two methods is very important for organizations. It helps them choose the best option for their situations.

Codeless testing has many benefits. It makes testing more accessible. On the other hand, traditional coding testing tools give better flexibility and control over the testing process. Organizations should think about these trade-offs carefully. They need to decide which option will support their software development goals best.

Understanding Key Use Cases

When to use Codeless testing tools

Best for teams with minimal technical knowledge.
You need to make tests quickly.
You need to increase test coverage fast.
Short-term project.
The workflow of your test project is standard and not complicated.
You are full of simple and repetitive test cases.
Codeless tools might make regression testing simpler.
You widely use or plan to implement AI-powered suggestions and self-healing capabilities.
Similarly, you need built-in CI\CD and third-party integrations to automate your workflow and set up some tasks there (for instance it might be parallel execution, analytics dashboards).

When to use Traditional coding testing tools

Best for teams with strong programming skills.
Your test project workflow is complex and needs tricky customization.
You need advanced debugging with detailed logs & stack traces.
You need deep integration with your company infrastructure.
Your App requires manual validation.
You have unique testing tasks.
Performance testing, security testing, complex API testing.
Legacy project technologies.

When picking a method and tools, think about what your project needs and your team’s expertise. Organizations should look closely at their testing needs, skills, and future goals to find the best approach for their situation.

Transition from Code-Based to Codeless Testing Methods

The shift from code-based testing to codeless testing needs careful planning. You should look at the organization’s culture, current tools, and skills of the team. It is best to use a phased approach:

1. Start with pilot projects and gradually implement codeless testing.
2. Find areas in the testing process that will benefit from codeless methods. These areas may include repetitive test cases, regression testing, or features with changing UI elements. Determine — what can and cannot be automated.
3. Identify what you aim to achieve with codeless automation.
4. Select the right codeless testing tool. Ensure the tool supports features you need like AI-powered test maintenance, parallel execution, and CI/CD integration. Consider scalability, ease of use, and reporting capabilities.
5. Also, it is important to give proper training and support to team members moving to codeless testing. Organizations should invest in training programs. These should teach the features of the codeless tool, best practices for codeless test automation, and address any worries about the new approach.
6. Schedule periodic test reviews by the whole team and refactoring to keep test cases relevant.
7. Of course, continuously refine your strategy

Challenges Codeless Testing Implementation

Codeless testing solutions can offer many benefits. However, organizations may run into some problems when they try to implement them. It is important to predict these problems upfront.

• Limited customization and flexibility. Many codeless tools provide pre-defined test flows, making it difficult to handle complex test scenarios.
  Advanced logic and customization may still require coding.
• Performance and load testing gaps. Most codeless tools focus on functional and UI testing but do not support performance, security, or API testing as easily as coded frameworks.
• Scalability issues. As test cases grow, managing and maintaining them in a codeless framework can become difficult. Some tools lack test management and collaboration features for large teams.
• Vendor lock-in. Many codeless platforms have proprietary formats, making migration to other tools challenging.
• Limited integration compatibility. Compatibility with some frameworks, third-party integrations, CI\CD pipelines, cloud environments, and DevOps workflows may be limited. Some tools do not support end-to-end testing across multiple environments.
• Lack of advanced debugging features in comparison to traditional coded automation. Identifying failures in complex workflows can be time-consuming.
• Complicated Test Maintenance. Record-and-playback tests may break when UI elements are modified. Its maintenance still may require manual updates.

To overcome appearing difficulties and maximize the benefits of codeless testing initiatives while mitigating challenges, follow these best practices: 

• Choose the right solution for your needs. Select a codeless platform that supports complex workflows, custom scripting (if needed), and seamless integrations with CI\CD. Choose tools that support test data export and can be migrated if needed. Ensure test scripts and their elements are reusable. Prioritize solutions that offer self-healing and modern AI capabilities to reduce maintenance efforts. Ensure the tool has long-term business goals and active community support.
• Combine codeless and traditional coded testing. Use codeless automation for repetitive UI and regression testing. Rely on traditional coded frameworks for complex logic, API testing, and performance testing.
• Implement a robust test strategy. A well-structured test strategy ensures that codeless testing tools enhance efficiency while maintaining high test coverage and reliability.
• Enhance debugging and reporting. Opt for tools with visual logs, screenshots, analytics dashboards and error-tracing features. Integrate with defect management tools like Jira to streamline issue tracking. Use analytics dashboards to identify gaps.
• Ensure scalability and modularity of test design. Structure test cases using reusable components to reduce duplication and maintenance overhead. Regularly review and update test cases to prevent outdated or flaky tests. Verify that the tool can handle large test suites and supports parallel execution. These parts can be easily shared and used in different test cases and projects. This helps to keep things the same and lessen repeated work in testing.
• Train and upskill QA team. Open communication is key to successfully managing different challenges. Continuous learning and a readiness to adjust are also important, so provide hands-on training.

Finally, teams have to talk about worries. Codeless tools have changed to provide enough flexibility for different testing situations.

Doing thorough research and trying out a proof-of-concept can help find and solve possible issues early. Most leading codeless testing tool vendors offer detailed guides and resources to help users integrate their tools and manage any compatibility problems. Also, provide Demo sessions of their products.

Future Trends in Codeless Testing

Codeless testing is set to reshape how software development works. Expected codeless testing will make the testing process easier for more people, no matter their coding skills

It is based on new technologies like AI and ML. There is also a growing need for faster and better testing. As the codeless testing field grows, organizations must stay updated on these future trends to remain competitive:

• Better Integration with DevOps & CI\CD – codeless testing tools will become more compatible with different communication platforms, project management, and development platforms especially note CI\CD, enabling seamless test execution in Agile and DevOps workflows.
• Adopting Shift-left testing and early automation – codeless testing will integrate earlier in the development cycle, helping teams detect and fix issues before they reach production.
• Enhanced test maintenance and self-healing – means tests can adjust to changes in the application’s user interface (UI) or features without needing someone to do it by hand.
• Support complex test scenarios, including API testing, performance testing, and end-to-end automation across multiple devices.
• Greater support for Cloud and SaaS-based testing – cloud-native codeless tools will strive to allow teams to execute tests from anywhere with scalable infrastructure.
• Hybrid testing models – it is a merging of codeless and code-based testing methods. Codeless tools will likely add better scripting options. This change will allow for more customization and flexibility when necessary.

Additionally, codeless testing automation tools need to keep up with new trends like AI, ML, and IoT. As these technologies become more common in software apps, testing tools and become very important in improving codeless tools. Let’s deep into more detail

AI-driven codeless testing will help to advance what we can do, namely:

→ generate tests
→ analyze testing data
→ find patterns
→ learn earlier test runs
→ provide insights
→ detect errors intelligently
→ predict future issues

These skills will be key to keeping test automation strong and cutting down the time needed for test upkeep.

Success Stories with Codeless Testing Tools

Many companies in different industries are using codeless testing tools. They want to improve their Quality Assurance (QA) processes and reduce the time it takes to release their products. This also helps raise the quality of their software. These success stories show clear benefits for companies that choose a codeless testing method. They show how this approach can improve a business’s profits.

These real-life examples highlight how codeless testing can change the game. They offer important ideas for companies that want to use this method in their software development and quality assurance efforts.

1⃣ Case Study: How a FinTech Startup Streamlined QA Processes

A FinTech startup was growing fast and needed to improve its QA processes. They wanted to make software testing faster and more accurate. However, they had problems with traditional testing methods. These methods took too much time and were hard to change as their development cycles sped up.

To fix this, the startup chose a codeless testing platform. This platform helped them automate much of their testing process. By using its easy-to-use interface and ready-made test modules, they could create and run test cases quicker. This change saved them time and effort.

Thanks to moving to codeless testing, the startup saw great improvements in their QA work. Their testing cycle times fell, which helped them release new features and products faster.

2⃣ Case Study: Achieving Unprecedented Test Coverage in E-commerce

An online store wanted to make its website better and easier to use. They had one big problem: making sure their web application worked well on all browsers, devices, and operating systems. It was very important for them to do enough testing to succeed.

To fix this, the online store used a codeless testing tool that allowed for cross-browser testing. This tool could create and run tests on many browsers and devices at the same time. As a result, they achieved a high level of test coverage like never before.

With this improved test coverage, their web application became more stable and reliable. By finding and fixing compatibility issues early, they could give a consistent and positive user experience to their customers on different platforms.

Conclusion

In closing, codeless automation testing tools have changed the way we test software. They save time and help both technical and non-technical testers work together better. These tools make continuous testing easier and allow for smooth code automation.

As we move towards a future with more AI, codeless automation testing tools are set to improve even more. While there are some issues with flexibility and compatibility, the good things about these tools are a lot.

To stay ahead in testing, look for the right codeless automation testing tool for your project needs and you will see a big change in your software development process.

The post Top Codeless Test Automation Tools for 2025 appeared first on testomat.io.

According to Fortune Business Insights, it is an argument that the AI-powered testing market is rapidly growing. While it was valued at $857 million in 2024, by 2032, it is projected to increase to $3,824 million. Impressive, isn’t it

Learn more about AI-powered testing in a video from an industry expert: Expert Talk on AI in Testing – Jason Arbon

Due to this trend, in this article, testomat.io team decided to highlight the immense potential of AI testing tools. We will also introduce you to various platforms that can optimize your testing efforts.

AI Testing Tools Definition

AI-based testing tools are powerful solutions for enhancing the speed of your testing duties. They allow you to automate and optimize various aspects of stages of the Software Development Life Cycle. In this way, such platforms help QA teams ensure high software quality and demonstrate impressive speed carrying on through workflow.

AI-based tools combine the capabilities of machine learning, natural language processing, and other technologies based on artificial intelligence (for instance its hearts are GPT -4, ADA LLama 2, Ollama models etc.).

Especially for QA Managers AI enables to improve the accuracy of testing results, enhance test coverage, and gain other advantages.

Benefits of Using an AI-Based Testing Platform

Modern development teams are required to maintain exceptional standards of software quality. This can be challenging. AI testing tools are designed to make this task easier with a range of advantages.

Good scalability of AI engine

Surely, in the practice of almost all Agile teams, there have been instances of project expansion. Often, in such situations, the platforms used fail to handle the assigned tasks.

Choose an AI testing tool and this problem won’t concern you. These platforms handle generating, improving and identifying duplicates within numerous test cases very well. How is that possible? You reduce the need for manual testing and, as a result, speed up the QA process.

Improved test coverage

Artificial intelligence-based platforms can create complex test cases. They can cover a variety of scenarios you even can not imagine. Including boundary cases or invalid input data. Furthermore, such tools use self-learning algorithms. This means that you will be able to systematically increase test coverage.

Wide integration capabilities of AI tools

You can integrate AI-based tools with almost any of the platforms used in the project. For example, they work excellently in conjunction with the Jira bug tracking system, GitHub or the Jenkins continuous integration service.

Continuous learning and improvement

At the core of AI-based testing platforms are different self-learning algorithms. They are designed to improve over time through systematic self-learning. During testing, they analyze your App’s progress. Then artificial intelligence algorithms take it into account for subsequent improvement of test input data. Thus, the accuracy and effectiveness of AI test runs often surpass test results on conventional platforms.

Here’s what the CEO of testomat.io, Mykhailo Poliarush, says about the advantages of using AI-based testing tools:

“Use AI-driven tools to automatically generate and update test cases based on application changes. This approach reduces the manual effort involved in maintaining test suites and conducting regression testing — ensures that your tests stay relevant as the application evolves.”

Now it makes sense to list the key features of AI tools. They make them indispensable in modern projects.

What Are the Key AI Capabilities for Software Testing?

AI-based tools use machine learning, data analytics, and other advanced technologies. This enables artificial intelligence to improve several aspects of software testing simultaneously. Its key capabilities include:

#1: Test Case Generation Using Machine Learning

The principle of test generation on AI platforms is as follows. The system analyzes the code of the digital solution and user interactions. Then, it examines existing test cases and historical data if they exist. Based on this, it automatically generates new test scenarios relevant to the current version of the tested application.

Capabilities of AI tools in intelligent test case generation:

• Boundary case coverage. This capability enables AI-driven testing tools to identify issues that a human tester might inadvertently miss.
• Swift adaptation to codebase changes. As new features are introduced or bugs are resolved, the AI dynamically adjusts test cases to align not only with the latest version of the software. This ensures the tests remain accurate and relevant.
• Enhancing test coverage. The goal of any AI testing tool is to cover the most important parts of the application. So, we can prompt it as well. This ensures accurate testing compromising with effectiveness.

#2: Test Execution Optimization with Predictive Analytics

Test execution optimization is another primary task of AI-based testing tools. To achieve this, such platforms use predictive analytics. This is achieved through the use of predictive analytics, which analyzes historical data to identify patterns and assess the likelihood of specific tests failing.

The positive aspects of using predictive analytics are as follows:

• Dynamic adjustment of the testing strategy. Strategy plays a crucial role in the successful completion of tests. The correct sequence of executing test cases and achieving the desired results depends on the strategy’s effectiveness.
• Saving time and resources. Predictive analytics saves time during testing, which allows for faster releases and reduces project costs. Additionally, AI-based tools are scalable, eliminating issues associated with running large test sets.
• Increasing the efficiency of test data management. Teams no longer need to manually generate test data. By connecting an AI-powered platform, the necessary data can be generated automatically online. This is another way to save time and budget.
• Optimization of parallel execution. AI testing tools not only support parallel execution of tests but also go further. These platforms can decide which tests to run in parallel and which to execute sequentially, considering dependencies between tests. For example, one test may depend on the completion of another.
• Smart failure analysis. AI-based testing tools can precisely identify high-risk areas causing flaky tests. Moreover, they detect these issues in real-time during the testing process. Through detailed reporting, the built-in AI further saves time by providing immediate insights.

#3: Defect Prediction and Root Cause Analysis

AI testing tools can estimate the likelihood of defects occurring in particular modules of the application. For this purpose, the system analyzes historical data, code changes, and results from previous test runs.

Here is what an AI-based testing platform can do:

• Defect prediction. Artificial intelligence suggests to testers where problems are most likely to occur. This allows the team to focus on testing functions with the highest risk levels.
• Root cause analysis. The platform swiftly traces defects to their source, whether it’s faulty code, misconfigurations, or inaccurate test cases. This significantly speeds up the troubleshooting process while minimizing system downtime.
• Comprehensive defect analysis. Through detailed analysis, QA teams can make informed decisions to improve test coverage, efficiently resolve issues, and implement measures to prevent their recurrence.

#4: Continuous Testing in CI\CD Pipelines with AI

AI facilitates the seamless integration of the testing tool with CI\CD pipelines. This ensures continuous testing in projects.

Key features of AI testing tools in this area include:

• Test automation. With each minor update to the codebase, AI tools turn test execution automatically, helping QA engineers catch issues early in the software development lifecycle (SDLC).
• Real-time adaptation. AI tools adjust testing activities dynamically in response to code changes, ensuring relevance and efficiency.
• Accelerated feedback loops. With AI, testers can recognize the critical functionalities and problematic areas, gaining rapid insights and making informed decisions about which tests to execute.

#5: Self-Healing Test Scripts

AI-powered platforms are capable of creating self-healing test scripts, which automatically adapt to changes in the software code. This guarantees that automated tests stay dependable and current.

What this feature in AI testing tools provides teams:

• Automatic script updates. When a test fails due to code changes, the AI modifies the test script autonomously, aligning it with the new conditions.
• Reduced resource consumption. Teams save time and effort on manually updating test scenarios, ensuring reliability even in fast-paced development environments.
• Improved accuracy. By increasing test coverage and ensuring test execution, AI testing tools deliver highly precise results.

These are the core features inherent in many leading platforms. Others may vary from tool to tool. To help you select the optimal solution, we present a list of the best AI testing tools with advanced capabilities.

Top AI Software Testing Tools to Streamline Test Process

The modern market for intelligent test automation solutions offers a wide variety. It includes platforms capable of testing various types, such as performance testing, API testing, regression testing and more. Some are designed for assessing the quality of web applications, while others are suited for detecting defects in mobile apps.

We believe that such tools represent the future of software testing. This viewpoint is shared by ASTQB experts:

Software testing is experiencing a transformative change with Artificial Intelligence (AI) at the forefront. Gone are the days when AI was merely a concept of the future. Today, it is a dynamic force reshaping error detection and correction, heralding a new chapter in software development.

Below, you will find a list of tools that are sure to meet the needs of your team.

Testomatio

The testomat.io is a powerful test management system that leverages artificial intelligence capabilities for test automation and efficient test case management.

AI enables users of the platform to automate test documentation, automatically generate test code, and analyze their projects.

List AI-based generative features to simplify & improve your workflows:

• Automatic Living Documentation generation. The system analyzes test cases and generates documentation in real time based on them. This eliminates the need for manual documentation, its maintenance, reducing time costs.
• Test description creation. Testomat.io can also analyze test code and generate human-readable descriptions of tests. This allows non-technical team members to participate in testing.
• Automation code generation. This feature is the reverse of the previous one. Provide the system with a test description, and it will recommend the pieces of code to automate that test.
• Responding to project queries. With this TMS, you can obtain any information about the testing progress in seconds. Extract data, identify and resolve issues — all without the need to manually sift through large amounts of data.
• Failure detection and root cause analysis. The system examines test logs and explains the causes of failures based on them. This significantly reduces debugging time.
• Test set compliance control with related Jira issues. This test management system features bidirectional integration with Jira. Users can link tests to Jira issues and run them directly from the project management system. AI ensures that testing efforts comply with the requirements documented in Jira and other bug-tracking tools, for instance, more modern Linear. This is crucial for tracking testing effectiveness.

Katalon Studio

Katalon Studio is an AI-based testing tool positioned by its creators as an “all-in-one test automation solution.” And it truly is — this platform is suitable for quality testing of web, desktop, and mobile applications.

Artificial intelligence optimizes testing efforts with Katalon. Its capabilities are aimed at test automation, intelligent test execution, and test result analysis.

Here are the main AI Katalon features:

• Object identification. In the process of test automation, the platform recognizes objects using AI algorithms. This improves the reliability of tests by minimizing their dependency on static locators. In other words, when the elements of the user interface change, the tests will be less prone to failures.
• Self-Healing Tests. The tool automatically detects changes in various system components and adapts the object locators accordingly. This reduces the need for manual efforts to maintain the relevance of test scripts.
• Test execution optimization. In this area, Katalon Studio’s capabilities are limitless. The platform can eliminate duplicate and redundant tests. AI also plays a key role in creating effective testing strategies. For example, test case prioritization is available, meaning the team can first test the most critical functionality. If needed, tests can be parallelized.
• Automatic test case generation. The tool takes the application’s functionality as the basis and generates test cases based on it. This speeds up the creation of large test sets, as it doesn’t require manual intervention.
• Visual testing. Within this QA process, AI detects discrepancies or regressions in the user interface. It compares screenshots with current UI visual elements. This way, you can identify defects that may not be found during standard functional testing.

Applitools

Applitools is a comprehensive solution that combines leading technologies for automating test creation and test maintenance in modern Agile projects. Among them are visual AI, GenAI, and no-code methods. This streamlines the testing process, minimizes the reliance on manual testing, and broadens test coverage.

Here is how the Applitools tool’s AI features make life easier:

• Smart Image Comparison with Visual AI. The platform leverages machine learning to compare baseline screen images with test snapshots, spotting even the tiniest changes.
• Automatic element identification. It recognizes user interface elements on its own, saving time and reducing effort. This approach helps increase the efficiency of visual testing by reducing the number of false positives and false negatives.
• Handling dynamic elements. Applitools can handle dynamic content, including animated elements. Unlike static elements, they change over time. Therefore, the tool only detects significant changes
• Accessibility Testing. Visual AI helps in testing the visual accessibility of applications, which is important for users with disabilities.
• Cross-browser and cross-device testing. Thanks to AI, testers do not need to manually configure each test variant. Applitools does this automatically.

Testim

Testim.io is a test automation tool that leverages artificial intelligence to deliver quality software products. With this ALM platform, you can test Salesforce, web, and mobile apps. This versatility makes the solution a natural choice for many teams.

What AI features does Testim offer to users? Here are the key ones:

• Self-Healing tests. Even if a user interface element changes in some way, the tests will continue to run successfully without manual adjustments. This simplifies test maintenance, which is especially important in large-scale projects.
• Identifying elements using smart locators. Using machine learning, Testim.io ensures the proper functioning of tests even when the UI structure changes.
• Identification of unstable tests. AI empowers the platform to pinpoint genuinely problematic tests while ignoring temporary glitches, helping teams focus on what really matters.
• Automatic test case creation. It observes user interactions, documents actions, and transforms them into test scenarios — saving you the hassle of writing tests manually.
• Plain English to test code. Simply describe the function you want to test, and the platform will generate the necessary code for you.
• Optimization of existing tests. AI helps quickly identify issues in test cases, improving debugging speed
• AI-powered visual testing. The tool detects even the slightest changes that may negatively impact the user experience.

Mabl

The Mabl platform uses generative AI and other artificial intelligence based technologies to enhance test automation. They optimize all aspects of testing, including test case generation, test maintenance, and test execution.

AI-powered features of Mabl that users can take advantage:

• GenAI test generation. QA teams can simply use natural language to automatically create test cases, no matter how complex the testing scenarios are.
• Early detection of unstable tests. The AI swiftly spots potential failures, alerting the team before problems escalate. This helps increase the stability of testing.
• Auto-healing. The platform automatically adapts tests to changes made in the code. This approach significantly simplifies test maintenance.
• Visual testing. The tool clearly identifies unexpected UI changes by comparing the baseline with the current version.
• Availability analysis. Mabl identifies accessibility issues on digital solution pages and prioritizes them. This allows development teams to understand which issues should be addressed first.
• Intelligent waiting. The tool tracks product performance and includes this information in the testing process timeline. This improves the reliability of tests.

Copado

Copado is a platform focused on DevOps and CI\CD for the development, testing, and deployment of Salesforce solutions. AI is utilized throughout each stage of the digital product lifecycle. However, since we’re focusing on testing, let’s dive into how it enhances that area specifically.

Copado benefits from the following its AI-driven features:

• Prioritization of test cases. The platform automatically identifies and selects tests that are most relevant, helping teams save time and resources.
• Quick creation and modification of test scenarios. The tool provides easy prompts for quickly creating or adjusting complex test scenarios.
• Predictive analytics. AI analyzes test results and predicts potential defects, allowing teams to spot issues early and address them proactively.
• Code quality assessment. The platform assesses the quality of test code and offers suggestions for improvement.
• Test conversion. The tool helps convert tests to different formats, making it easier to adapt to various testing environments.

AI Test Generator

AI Test Generator is a tool that can significantly ease the life of QA teams. Its task is intelligent test case generation for different types of testing. Users note its impressive ease of use and versatility. With this solution, you can automate various types of testing, including functional testing, regression testing, performance testing.

Here are its key AI Test Generator features:

• Automatic test generation. The platform creates test cases based on input data or user requirements. AI also analyzes system behavior to identify which scenarios need testing.
• Optimization of test runs. The AI Test Generator looks at historical data, identifies patterns, and recommends the most relevant and effective test cases for priority execution.
• Automatic identification of test cases. The platform ensures no critical test cases are missed due to human error, improving overall test coverage.
• Automation of test case creation within CI\CD pipelines. The tool integrates seamlessly into a project’s CI\CD pipeline, ensuring the correct tests are generated and executed automatically during workflows.
• Test case customization. While the tool automates test generation, testers can still define parameters like application type, testing goals, and more.
• Test documentation creation. The platform automatically generates documentation for the testing process, making it easy for the team to review test descriptions, expected outcomes, and steps.

AccelQ

AccelQ is a cloud-based test automation tool that utilizes artificial intelligence in its processes. The platform is designed to ensure continuous testing on your project, focusing on high-quality automation. It is suitable for web and mobile testing.

Key AI features of AccelQ include:

• Self-healing tests. The platform tracks even the slightest changes in the user interface and automatically adjusts the test according to the current code base. This process requires no manual intervention and reduces the effort needed for test maintenance.
• Dynamic object identification. AccelQ can identify UI elements in real-time during software testing, supporting a variety of devices and browsers.
• Automatic test case generation. The solution analyzes the system’s behavior and, based on its functions and processes, creates relevant tests. This accelerates the testing process and improves test coverage.
• Test run prioritization. AccelQ tracks code changes and identifies which tests should be prioritized based on those updates.
• Predictive analytics and AI reporting. The tool examines historical data to highlight high-risk areas and generates detailed reports on testing progress and outcomes.

Each of these tools offers unique features and varying costs. When selecting an AI testing tool, it’s important to strike the right balance between price and quality. Many platforms also offer free trials to help you make the best choice.

Potential Issues of AI-Based Testing

AI testing tools have a vast number of advantages. However, is everything as flawless as it seems? If you’ve decided to integrate artificial intelligence into your QA processes, you should be aware of the potential risks that you may encounter.

• Lack of Human Control. AI-based testing is effective, fast, and simple. However, you should not blindly rely on artificial intelligence. For example, when it comes to user experience, AI may overlook some important details.
• Limitations in Handling Edge Cases. Most tools excel at testing standard test scenarios. However, not all test automation tools are capable of handling edge cases effectively.
• Dependence on the Quality of Machine Learning Model Training. The effectiveness of AI-based testing depends on the quality of the data on which the machine learning model has been trained. If the data quality is low, the results may be inaccurate.
• Opaque Nature of Blackbox AI. Many test automation platforms use machine learning models that function like a “black box,” meaning their internal processes cannot be interpreted by humans. As a result, it may be difficult to trust the obtained results.
• Data Privacy and Security. AI-based testing involves processing large datasets, often containing confidential information. This raises concerns regarding its security.

Bottom Line

AI testing tools are changing the way testing process is done in today’s Agile projects, making it simpler and faster. These tools can handle complex test scenarios, work with large datasets, and adapt to different types of testing across a wide range of devices. Teams using AI-driven testing have reported faster QA workflows, fewer errors, and improved product quality.

When selecting the right tool, be sure to evaluate its functionality and cost. The good news is, you will start seeing the results of your investments quickly.

The post AI Testing Tools: An Effective Way to Optimize Your QA Processes appeared first on testomat.io.

However, in addition to their common characteristics, they have significant differences. For example, Puppeteer is focused on automating Google Chrome and Chromium browsers, while Playwright offers cross-browser capabilities.

Next, we will take a closer look at the features of each framework and determine which one is best suited for the testing process in your project.

What to Consider When Choosing a Library

If you are at the stage of selecting a tool for test automation, rely on the following criteria:

1. Support for Required Test Types. Each tool, due to its functionality, may not be suitable for all tasks. Therefore, define in advance what type of tests you will need.

2. Learning Curve and Ease of Use. Before starting, evaluate the community of the specific tool, its documentation, and guides. As for ease of use, test the user interface, API, etc.

3. Compatibility and Integration. Identify which browsers and platforms the tool supports. It is also essential to evaluate how seamlessly it integrates with third-party services, such as CI\CD pipelines or testing frameworks.

4. Functional Features and Performance. Assess whether the library offers functionalities that can enhance testing efficiency. This could include parallel test execution, working with headless browsers, etc.

5. Scalability. If the number of test cases on the project is expected to increase over time, ensure that the library can handle the growing load.

6. Cost and Licensing. There are open-source libraries and paid solutions on the market. Evaluate the available functionality and decide whether a free tool is sufficient for you.

We hope these recommendations will help you choose the right tool for web automation tasks. Now it’s time to discuss the capabilities of the two specific frameworks – Puppeteer and Playwright – and finally make your choice.

What is Puppeteer? Key Features of the Framework

Puppeteer is a Node.js library created by Google in 2017 and is currently maintained by the Chrome dev team. It provides a set of APIs for interacting with web pages. Among the key browser automation tasks are web scraping and automated testing in both headful and headless modes.

On the official website, the tool is defined as:

Puppeteer is a JavaScript library which provides a high-level API to control Chrome or Firefox over the DevTools Protocol or WebDriver BiDi.

Puppeteer framework supports easy integration with popular JS testing environments. Together with its extensive functionality, this makes it a flexible tool suitable for different types of testing.

Here are the prominent features of Puppeteer

• Simulating User Actions. The tool can replicate a wide range of user interactions, such as clicking buttons, filling out forms, handling alerts, navigating pages, and more. This makes it ideal for end-to-end testing, as it ensures the validation of all user flows within a product.

• Full Automation. Puppeteer automates the entire workflow, from opening web browsers to interacting with elements on web pages.

• Testing in Headless Mode. The tool supports working with headless browsers, meaning browsers without a visible user interface. This improves testing speed. However, for certain tasks, such as debugging or visual inspection, tests can be run in headful mode.

• Simple Interaction Methods with DOM Elements. Puppeteer provides simple methods for interacting with DOM elements, including clicking buttons, entering text, selecting options, and more, making it an excellent choice for UI testing.

• Wait for Elements. The tool can halt execution until specific elements appear in the DOM, which is particularly helpful for applications with asynchronous processes where elements are dynamically loaded.

• Navigation Control. Puppeteer enables programmatic navigation between web pages, ensuring that the application performs correctly during different user journeys.

• Network Interception. The tool can intercept and modify network requests and responses. This feature is valuable for testing how an application reacts to different network conditions, such as slow connections or server failures.

• Simulating API Responses. Puppeteer allows you to simulate internal scenarios without needing a real server. It generates mock API responses, enabling QA teams to conduct quick, isolated tests.

• Screenshots. The framework allows users to take screenshots of entire pages or specific elements. The former can be compared to baseline images to detect visual changes, while the latter can be used to check individual components. This enables visual regression testing.

• Performance Measurement. Puppeteer is excellent for performance testing, as it can measure various metrics like page load times or time to interactivity. It also collects browser performance metrics, such as CPU usage and memory consumption.

• Cross-Browser Testing. This is not Puppeteer’s strength, as it is primarily focused on Chromium and has limited Firefox support. Therefore, if you need to test applications in other modern browsers, this tool may not be suitable. Other frameworks would be a better option.

• Device Emulation. The tool allows you to emulate mobile devices and tablets, including their screen sizes and geolocation data. This enables testing of specific features and designs. Additionally, users can modify the User-Agent string to simulate different browsers and devices.

• Parallel Testing. Puppeteer provides different browser contexts, making parallel test execution possible. This option, combined with headless mode, speeds up the process and makes Puppeteer suitable for CI pipeline integration.
• Debugging Features. Puppeteer users can view browser execution in real-time, provided they use headful mode. It allows pausing test execution to debug script issues. If you choose this tool for your project, you will have full control over browser functions through access to the Chrome DevTools Protocol.
• Automatic Error Handling. Puppeteer allows handling common errors (such as page timeouts, elements not found, etc.) without manual intervention. Tests can be retried or marked as failures.

All these features make the library the number one choice for many teams. Let’s explore its advantages and disadvantages so you can make an informed decision.

Puppeteer: Key Advantages and Disadvantages

The Advantages of Puppeteer include high testing speed and extensive capabilities for QA teams. Let’s take a closer look at them

1. Cross-platform support. The tool works on various operating systems – Windows, macOS, and Linux. This makes it compatible with many environments.

2. Ability to conduct various types of testing. Puppeteer allows for E2E, API, UI, and performance testing. We will discuss this in more detail later.

3. High testing speed. This is facilitated by some of Puppeteer’s features, including testing in headless browsers, automatic error handling, parallel testing, API response simulation, and more.

4. Integration with popular JavaScript testing frameworks. Puppeteer integrates easily with Jest, Mocha, Jasmine, and other frameworks. This allows for easy writing and effective management of test cases.

5. User-friendly interface. The tool offers a convenient API. It allows automating browser actions with minimal code.

6. Extensive community support. Puppeteer has a large user base, an active GitHub repository, and detailed documentation. Any questions can also be asked on numerous forums, chat groups, and discussion platforms.

7. Moderate learning curve. Since Puppeteer is based on the DevTools protocol, it will be easy for anyone familiar with Chrome Developer Tools to get started with it.

In addition to the above advantages, the library has some Puppeteer Drawbacks:

1. Limited browser support. Puppeteer is not exactly a cross-browser tool. It is focused on automating Chromium and Google Chrome. Although the latest version does offer experimental Firefox support.

2. Dependency on Node.js. Since Puppeteer is a Node.js library, a corresponding environment is required for it to function. Therefore, this tool is not suitable for teams that do not use Node.js as their main technology stack.

3. Limited mobile device emulation capabilities. The tool offers this feature, but it is quite limited. For example, other frameworks (e.g., Playwright) can emulate more devices and handle touch gestures more comprehensively.

4. Challenges with testing interactive applications. Puppeteer can simulate most user actions. However, when it comes to testing complex scenarios, additional configuration may be required.

5. Lack of built-in support for multiple browsers. Running tests in different browsers, such as Chrome and Firefox, requires configuring multiple instances of Puppeteer.

Consider all the disadvantages of Puppeteer and evaluate how critical they are for the quality of work on your project.

Using Puppeteer Legally

Puppeteer is often primarily associated with complex web scraping activities. The legality of such activity can be questioned depending on various factors:

• the terms of service of the target website
• the goals of scraping (copying content may violate copyright laws)
• the specific laws of a country (for example, in the US, scraping can lead to civil or criminal sanctions under the Computer Fraud and Abuse Act)

For the use of Puppeteer to be legal in this context, it is necessary to limit the collection of publicly available data or obtain permission from the website owner.

However, it is also important to remember that another legal use of Puppeteer is web testing. Let’s explore in detail what QA activities the tool supports

Testing with Puppeteer

Puppeteer allows teams to apply a flexible approach to testing various aspects of application performance. Look at how the tool can help ensure premium quality for the final product.

Testing Mobile Progressive Web Applications (PWA)

In the context of testing PWAs, Puppeteer focuses on ensuring that these applications behave like native mobile apps. With the tool, you can test the following features of progressive web applications:

• the operation of service workers under different conditions
• offline functionality (without an Internet connection)
• the correct installation of the app (e.g., displaying the “Add to Home Screen” prompt)
• the functioning of push notifications
• correct use of the PWA manifest file

End-to-End Testing

Puppeteer allows you to simulate real user interactions with the application. The simulations can include any actions, from opening web browsers to filling out/sending forms and navigating web pages.

E2E testing with Puppeteer ensures that all processes in the application work flawlessly, as expected.

API Testing

The tool is primarily used for browser interactions. However, another key feature of Puppeteer is simulating network requests and API calls. This should be done using the page.setRequestInterception(true) method. It allows for checking the correctness of requests and the adequacy of responses.

Performance Testing

Puppeteer is well-suited for performance testing. It integrates easily with powerful tools such as Lighthouse to conduct performance audits. You can also use Puppeteer’s own APIs to gather relevant metrics:

• time to load web pages
• time to first meaningful paint (FMP)
• time to interactive (TTI)
• response speed across different devices.

UI Testing

The Puppeteer library can also be used for testing UI components. With the automation of user actions, you can check the responsiveness, visibility, and interaction of elements under different conditions.

The advantage is that the tool offers advanced features that allow you to optimize the execution of tests of this type. This includes taking screenshots and recording videos during the testing process.

Next, let’s explore testing with Puppeteer through a practical example.

Your First Test With Puppeteer: Code Example

Working with Puppeteer starts with installing it. You’ll need the npm package manager for this:

npm install puppeteer

Then, set up the environment by following the guide on GitHub:

node server.js

Next, here’s a code example to test the functionality of logging into a website. This requires performing two steps:

1. Create the file test-login.js
2. Add the following code:

const puppeteer = require('puppeteer');
(async () => {
  const browser = await puppeteer.launch({headless: false});
  const page = await browser.newPage();
  const timeout = 5000;
  page.setDefaultTimeout(timeout);

  try {
    await page.goto("http://localhost:8000");
    await page.waitForSelector(`#fname`, {timeout, visible: true});

    await page.type(`#email`, `your email`);
    await page.type(`#password`, `your password`);

    await page.screenshot({path: "./login.png", fullPage: true});

    await Promise.all([
      page.click(`input[name="loginSubmit"]`),
      page.waitForNavigation()
    ]);

    const url = await page.url();
    if (url !== 'http://localhost:8000/login') {
      throw new Error(`The URL was not http://localhost:8000/login`);
    }

    console.log(url);
  } catch (err) {
    console.log(err);
  } finally {
    await browser.close();
  }
})();

Code example for testing login functionality

What functions did we use?

• type()– for entering text into the corresponding field;
• click()– for clicking the “Login” button;
• waitForNavigation()– for waiting for the navigation to a new browser page.

Now, let’s look at how to automate testing with Puppeteer using the above example. To do this:

1. Create the file test-login-automated.js
2. Add the following code:

const puppeteer = require('puppeteer');

(async () => {
    const browser = await puppeteer.launch({headless: false});
    const page = await browser.newPage();
    const timeout = 5000;
    page.setDefaultTimeout(timeout);

    try {
        await page.goto("http://localhost:8000");
        await page.waitForSelector(`#fname`, {timeout, visible: true});

        // Fill in email and password fields
        await page.type(`#email`, "your email");
        await page.type(`#password`, "your password");

        await page.screenshot({path: "./login.png", fullPage: true});

        await Promise.all([
            // Click on login button
            page.click(`input[name="loginSubmit"]`),
            
            // Wait for page to navigate to the next page
            page.waitForNavigation()
        ]);

        // Get url of the page
        const url = await page.url();
        if (url !== 'http://localhost:8000/login') {
            throw new Error(`The URL was not http://localhost:8000/login`);
        }

        console.log(url);
    } catch (err) {
        console.log(err);
    } finally {
        // Close browser window
        await browser.close();
    }

Code example for automating tests with Puppeteer

3. Run the code using the command:

   node test/login.js

Congratulations! You have just written your first test with Puppeteer. Now, let’s explore how you can optimize the testing process with this library.

How to Optimize Your Tests With Puppeteer?

We’ve gathered the best practical tips to improve the speed and efficiency of testing with Puppeteer:

Do not overuse the waitFor* Excessive usage can slow down the testing speed.
Limit the number of actions performed in each test. This will help improve performance.
Run tests in parallel and use headless mode. This will also positively affect performance.
Pay attention to settings and updates. Properly configured test runners and code updated to the latest Puppeteer version ensure tests run correctly.
Study the documentation carefully. In addition to the library usage guide, it’s useful to review the Headless Chrome
Avoid using the library for web scraping, as it may be illegal.

Follow these tips, and testing with Puppeteer will meet your team’s expectations.

When Is It Worth Choosing Puppeteer?

Naturally, when selecting a tech stack for specific tasks, you should focus on the specific project requirements. However, in this section, we have gathered some general signs that Puppeteer is the perfect choice for your team.

• Simple projects focused on automating Chrome/Chromium. If you are working with these browsers, Puppeteer will be the best option for you.
• Projects focusing on Headless Mode. The tool supports working with headless browsers. This makes it an ideal choice for teams that value speed and high performance.
• Teams interested in a quick start. This library has been around for a long time, so it has community support, detailed documentation, and guides. Therefore, a steep learning curve is not an issue with Puppeteer.
• Sufficient resources available for the project. Testing with this tool is quite resource-intensive for your machine. Keep this in mind when choosing a library.

If your project does not involve working with dynamic content, you can consider using simpler frameworks (like Jest for unit testing). They require fewer resources and therefore can be more efficient.

What Does the Framework Offer to Automation Engineers?

Playwright is also a modern Node.js library that has been meeting the needs of users for testing and browser automation since 2020.

According to information provided on the official website of the framework — Playwright Test Automation framework was created specifically to accommodate the needs of end-to-end testing. However, this does not mean it only supports this type of testing.

This library stands out from Puppeteer due to its variety of supported browsers and a stable set of features, making it an ideal framework for complex projects.

Here are the key features of Playwright:

• Cross-browser testing. Playwright can work with Chromium, Firefox, and WebKit (Safari). This ensures that your application works consistently across different web browsers.

• Cross-platform. The framework supports various operating systems, including Windows, Linux, and macOS. Testing with this tool ensures that web applications will function properly on different devices.

• Automatic waiting. Before performing any action, Playwright waits for the elements to become active. This protects users from unstable tests.

• Web-first assertions. The framework allows you to repeat checks until all necessary conditions are met.

• Browser contexts. Playwright can work with multiple browser contexts simultaneously. This allows you to create isolated tests and speed up the testing process.

• Codegen. This feature enables you to generate tests by recording user actions without writing code. The tests can be recorded in any of the supported programming languages.

• Real device. Playwright provides built-in emulation of mobile devices and the ability to test responsive web designs.

• Network interception. You can intercept and manipulate network requests. This feature is particularly useful for testing edge cases.

• Support for headless and full mode. This makes testing with Playwright highly flexible, essential for running tests in different environments.

• Playwright inspector. This feature is key to obtaining detailed information about executed tests. It allows you to inspect pages, generate selectors, view step-by-step test execution, check execution logs, and more.

• Trace Viewer. This feature is essential for identifying the cause of failures during testing. Users have access to screen recordings, DOM snapshots, test code, and more.

• Imitating real user actions. The tool can simulate real user interactions with web pages, such as filling out and submitting forms, dragging elements, and more.

• Visualizing test results. Playwright supports creating screenshots of the entire page or individual elements, as well as recording videos. These tools help testers quickly understand the issue and pass the bug to developers for fixing.

To learn more about Playwright’s capabilities, check out this video: Test Automation with Playwright

Such multi-functionality and versatility often serve as arguments for choosing this framework for test automation. Let’s look at other advantages of Playwright and potential challenges when using the tool.

Pros and Cons of Playwright

Users often choose Playwright for testing on their projects because of these advantages:

1. Support for various testing environments. By working with popular web browsers and operating systems, Playwright ensures that the application works uniformly across different devices.

2. Working with headless and headful browsers. This allows the framework to be used for various tasks. Headless mode is ideal for speeding up testing and integrating into CI pipelines, while headful mode is suited for debugging and manual interaction.

3. High testing performance. This is facilitated by the ability to run tests in parallel and the support for browser contexts.

4. Reliability of tests. This is achieved by the automatic waiting feature, which eliminates the need to create artificial timeouts, a common cause of unstable tests.

5. Integration with popular JavaScript testing frameworks. Playwright can easily integrate with Jest, Mocha, Cypress, and other frameworks, simplifying work with test cases.

If you are planning to choose this framework for testing on your project, it is also important to familiarize yourself with the disadvantages of Playwright:

1. Limited community support. Since it is a relatively new tool, its user base is still developing. This also affects the availability of guides and documentation – they might be difficult to find online.

2. Steep learning curve. Beginners in the field of browser automation may require extensive training, especially when it comes to complex web automation tasks like network interception or working with multiple browser contexts.

3. Limited automation capabilities. Playwright is focused on automating web applications. If you are also working with mobile or desktop solutions, you may want to choose another tool.

4. Dependency on Node.js. Playwright requires Node.js to run. Therefore, it is mainly suitable for users working with JavaScript/TypeScript.

Playwright is a powerful library designed for automating modern browsers. It has its advantages and disadvantages, which should be considered when choosing the tech stack for your project.

More How to setup first Playwright project & details of framework follow our articles:

• Playwright AI Revolution in Test Automation
• Cucumber framework for API testing with Playwright example
• Playwright Test Automation: Key Benefits and Features

For Which Projects Is Playwright the Best Choice?

When should you choose Playwright to optimize testing? The following scenarios include:

• The project involves cross-browser testing on Chromium, Firefox, and WebKit.
• You need device emulation.
• The project involves complex interactions, such as network interception, multi-page scenarios, etc.
• You are interested in the highest possible testing speed.
• The team uses JavaScript/TypeScript and would benefit from Node.js support.

— Did you recognize your team in the criteria above? Feel free to choose Playwright – it will help achieve your project goals.

Puppeteer vs Playwright: Comparative Analysis (Table)

To systematize the information presented in this material, we offer you a comparative table of Playwright vs Puppeteer.

Supported OS	Windows, macOS, Linux	Windows, macOS, Linux
Browser Support	Chrome/Chromium limited Firefox support	Chromium, Firefox, WebKit (Safari)
Cross-Browser Testing	Limited support – within Chrome/Chromium and Firefox	Full support for all browsers mentioned above
Mobile Device Emulation	Supported	Supported, more user-friendly and reliable feature
Parallel Test Execution	Supported	Supported
Language Support	JavaScript/TypeScript, Python, C#, Go	JavaScript/TypeScript, Python, C#, Java
Headless Mode	Supported	Supported
Network Interception	Basic support	Advanced support with network request mocking and intercepting responses
Optimal Project Type	Simple Chrome/Chromium-focused projects	Cross-browser testing, advanced test automation
Primary Use Case	Chrome automation, simple web automation tasks	Complex workflows (parallel testing, user emulation, etc.)

Why Use Puppeteer and Playwright in Agile?

Using Puppeteer and Playwright in Agile projects can bring teams several benefits. These primarily include high testing speed, process automation, and optimal performance.

Here’s how these libraries can help achieve Agile development goals:

• High test execution speed. Agile methodology relies on iterative development and requires quick feedback. The tools in question allow for high testing speed, helping teams monitor application functionality during each iteration.

• Continuous Integration (CI) support. Puppeteer and Playwright integrate seamlessly into CI/CD pipelines. This enables automated testing during each build or deployment, ensuring continuous testing throughout the project lifecycle.

• Ensuring high application quality. Both libraries support testing across various OS and provide device emulation capabilities. Playwright also enables cross-browser testing. This ensures that QA teams can verify the product’s performance on any device.

• Support for the Shift-Left practice. Both tools allow teams to shift testing to earlier stages of the SDLC, aligning with Agile methodology principles.

• Process automation. Puppeteer and Playwright support test automation, helping reduce human errors and minimize the risk of regressions.

The Node.js libraries discussed in this article are well-suited for Agile projects. They provide teams with rapid feedback, facilitate frequent releases, and help create high-quality digital products.

Final Thoughts

— Which framework for web automation tasks is the best fit for your team? To answer this question, you need to consider the project requirements, tool functionality, ease of use, cost, and other criteria…

In this material, we have reviewed two Node.js libraries frequently used in Agile projects – Puppeteer and Playwright. Both are powerful tools with extensive capabilities in the field of web automation.

The post Puppeteer vs Playwright: Choosing the Best Library for Browser Automation appeared first on testomat.io.

Achieving these goals is impossible without a well-structured testing strategy, an important part of which is test automation. Moreover, the reality of today is such that automating tests is more optimal with the power of AI.

Artificial intelligence and generative AI may be the most important technology of any lifetime,

says SalesForce CEO Marc Benioff

— How are teams using AI testing technology in their workflow when using Playwright

Let’s explore further!

Codegen Playwright

Codegen — is a powerful built-in Playwright tool that automatically creates test cases by recording user interactions with a web application or website. Its usage like an AI tool is the most predictable. Playwright Codegen is not only fast, but also quite a smart testing tool

To get started with Playwright Codegen, you should run the next command in the terminal:

npx playwright codegen

This will open two windows: a browser, where you will perform various actions, and the Playwright Inspector. The latter will display the Codegen window filled with the code generated as a result of your interactions. It will look like this:

All these actions take just a few seconds. This speeds up the execution of testing tasks and allows automation engineers to implement best testing practices in their projects.

Anyway, it is important to remember — Playwright Codegen is not perfect, and all the code it generates should be double-checked for alignment with the team’s needs.

AI testing & Automation with ChatGPT

An alternative to classic test automation with Playwright Codegen is generating test code with AI. To do this, in a dialogue window – for example, the well-known is a GPT chat – plain text instructions should be entered with its interface. They should contain information about what exactly needs to be tested, which device configuration to use, how to write a particular piece of code or improove it etc.

Of course, the generated by AI Playwright tests may contain some inaccuracies in your testing scripts. For example, your AI code may use locators that are not recommended by the framework. These include page.click() and page.fill() Using the same plain text commands, you can point out the inaccuracies to GPT and ask it to correct them to the proper version – in our case, role-based locators.

To avoid constant revisions, it is advisable to include preliminary plain text prompts in the request to generate the Playwright AI tests. A small recommendation – example prompts can be found on cursor.directory:

Thus, to successfully generate Playwright tests using AI, you should:

use a specialized prompt to help the tool understand what it is dealing with;
verify the accuracy and relevance of the generated code;
provide as much context as possible (e.g., JavaScript and HTML files – anything that supports your app).

Given that modern web applications have highly complex architectures, fulfilling all these requirements can be challenging. Therefore, Playwright Codegen remains a more convenient option for the rapid creation of tests.

GitHub Copilot for Playwright Code Generation

Copilot AI is a powerful tool that analyzes the context of your code and then suggests complete Playwright test scripts that you can use for test creation, debugging, refactoring and maintenance of your code. It is seamless with all Microsoft Products.

For instance, Copilot identifies redundant lines, incorrect selectors, unused variables, or better Playwright-specific API methods to enhance maintainability and performance. Copilot AI may be trained on public code or your own. Moreover, Copilot’s alternative AI code can simplify intricate test scenarios, fix asynchronous issues or propose ways to handle flaky tests, saving time spent thinking up the best solutions at times.

Testomatio AI test management for your workflow

Recently, testomat.io team developed a couple of new AI features from testomat.io, including AI-powered test case creation with advanced analytics for QA test managers. These improvements provide smarter test management, increased productivity, and full project control.

It suggests optimized tests with AI for popular test automation frameworks like Cypress, Jest, Cucumber, PyTest, and JUnit etc, including the Playwright framework. AI feature automatically generates test case ideas based on the content of the test suite. Users can review the suggested tests, select the relevant ones and add them directly to the suite or reject them and re-generate the new ones. This accelerates test case creation, enhances test coverage by identifying overlooked scenarios, and reduces manual effort while maintaining high testing quality.

Why Testomatio AI Test Management insights worth implementing?

• Increased productivity. Save time with faster synchronization of your manual and automated test sets.
  Improved test quality. AI Flaky test detection identifies patterns in test execution to pinpoint tests that pass or fail inconsistently.
• Intelligent test maintenance. Maintaining test cases in dynamic environments can be tedious. Testomat.io’s AI identifies duplicates, obsolete or redundant test cases or test steps based on them. Test history trends over time and Analytics may empower teams about gaps in test coverage after updates.
• Faster Releases. Predictable analytics and smart prioritization help identify and address high-risk areas in test strategy promptly.
• Seamless Collaboration. Enable non-technical stakeholders to contribute using plain language test creation.

We provide a Custom AI Provider integration. It enables companies to use their preferred AI provider, such as OpenAI or Anthropic, for all AI-driven features. This feature offers flexibility in leveraging AI capabilities while ensuring compliance with data-sharing policies and regulations.

Also, as an alternative, you can use third-party AI-based tools designed to optimize work with the popular framework.

Other Third-Party AI Tools work with Playwright

Playwright itself does not provide users with artificial intelligence-based functionality. As a result, its enthusiasts have to turn to third-party tools to optimize web automation with their favourite library.

QA teams who find Codegen’s capabilities insufficient may consider AI-based automation testing tools like Auto Playwright or ZeroStep. Below you can see an example of a piece of code generated by ZeroStep AI-based tool.

Now, take a look at their features and advantages

ZeroStep

ZeroStep – is one of the most effective testing solutions that leverages the power of GPT-3.5 and GPT-4 for the rapid automation of Playwright tests.

Using the platform involves writing instructions in natural language. Simply tell the tool how you need to interact with the website, and AI will handle the rest for you!

To get started with ZeroStep, use the command:

npm i @zerostep/playwright -D

ZeroStep Playwright AI features for optimizing web testing

• Test development without writing code. All the user needs to do is interact with the application via the UI. The tool will generate the tests automatically.
• Cross-browser and cross-platform testing. The platform supports testing in all popular browsers (e.g., Chrome, Firefox, Safari) and on various platforms (Windows, macOS, Android). This guarantees that the digital solution performs consistently across various environments.
• Self-healing tests. The tool automatically adapts tests to minor changes in the product, minimizing the need for manual intervention.
• Seamless integration with third-party platforms. ZeroStep easily integrates with CI\CD solutions such as GitHub Actions, GitLab, Jenkins, etc.
• Detailed reporting and analytics. The platform provides users with comprehensive reports featuring visualized results. Additionally, information on test coverage, performance, and other analytics is available.

Advantages of ZeroStep AI

• Affordable pricing. Users get access to 500 free AI() function calls per month. If you need to increase the limit, the cost starts at $20.
• Optimized collaboration. The no-code approach allows non-technical team members to participate in testing.
• Increased accuracy in software testing. Smart test maintenance adapts tests to changes in the application. This helps keep them up-to-date and prevents inconsistent test results.
• Fast Feedback. Real-time test execution and integration with CI\CD pipelines enable teams to respond to issues promptly.

Auto Playwright

This is another tool that allows teams to integrate artificial intelligence into their Playwright workflows. It serves as an intelligent intermediary between the user’s plain text instructions and the test code.

This approach ensures high-quality software products in today’s dynamic digital landscape.

Setting up and configuring Auto Playwright is straightforward. To get started, follow these steps:

1. Install the dependency using the command:

   npm install auto-playwright -D

2. Create an OpenAI API key (you will need to register on the OpenAI platform for this).
3. Export the OpenAI API token by running the command:

   export OPENAI_API_KEY='sk-...'

4. Configure the environment variables following the guidelines provided in the official documentation for the tool.

After completing these steps, you can use the Auto Function to create tests.

Let’s look at a practical example of code generated by Auto Playwright. Suppose we need to verify the correctness of the user registration process in an application.

To generate an automated test case, compose instructions containing the following directives for the tool:

→ navigate to the registration page;
→ fill in the user details;
→ submit the registration form;
→ verify the results of checks – a success message should be displayed upon registration.

The generated Auto Playwright AI test will look like this:

import { test, expect } from "@playwright/test";

import { auto } from "auto-playwright";



test('User registration', async ({ page }) => {

 await auto('go to the registration page', { page });

 await auto("fill in name, email, and password", { page, test });

 await auto('submit the registration form', { page });

 const registrationSuccess = await auto('is the registration successful?', { 

    page, 

    message: 'Registration successful' 

  });



expect(registrationSuccess).toBe(true);

});

Learn more about the tool by watching the Execute Automation Academy by Karthik K.K video: Auto-Playwright – Run Playwright Test with AI (Using GPT 3, GPT 4 Turbo)

Key features of Auto Playwright:

• Automatic waiting. The tool automatically waits for elements to be ready for interaction, reducing the number of flaky tests.
• Cross-browser testing support. You can run tests in Chromium, Firefox, and WebKit to ensure your solution works consistently across different browsers.
• Code reduction. Auto Playwright streamlines page interactions and reduces repetitive code. For instance, interacting with a registration form field only takes a single line of code instead of multiple lines. To demonstrate, let’s refer to the test from the previous example:

 await auto("fill in name, email, and password", { page, test });

• Automatic element visibility checks. You do not need to write specific assertions for visibility, reducing the complexity of test cases.
• Retry on failures. The tool retries test executions in case of common errors (e.g., browser load failure, element timeout, etc.). This makes tests more reliable and stable.
• Easy navigation. Auto Playwright supports navigation between different pages, tabs, or browsers.

Advantages of Auto Playwright:

• Cost savings. Auto Playwright is an open-source framework, meaning you can use this effective testing solution completely free of charge.
• Improved collaboration on the project. With this Playwright AI tool, you can create readable, concise autotests. This simplifies the work for less experienced testers and allows non-technical specialists to participate in testing.
• Reduced risk of errors. This is facilitated by the advanced features of the Auto Playwright AI tool, including auto-waiting, element visibility checks, retries, and more.
• Wide integration capabilities with testing frameworks. You can leverage Jest, Mocha, Jasmine, or other platforms while benefiting from the power of AI with Auto Playwright.

Comparison of ZeroStep & Auto Playwright

To help you choose the optimal tool for your project, here’s a comparison of ZeroStep and Auto Playwright in a clear tabular format.

What Are the Limitations of Tools for Playwright AI Testing?

In this article, we’ve provided plenty of evidence that artificial intelligence is the future of test automation. However, it’s crucial to consider the limitations that might emerge when using AI-based testing tools. Let’s take a look at the potential challenges teams may face when deciding to implement AI in their projects.

Limitations of ZeroStep:

• Limited User Capabilities. The tool uses a codeless approach to writing test cases. As a result, it may be difficult to automate complex and non-standard workflows.
• Scalability Challenges. The tool is primarily designed for small and medium-sized projects, and managing large test sets can present challenges.
• Basic Integration Capabilities. Users may experience difficulties integrating ZeroStep into their workflows.

Limitations of Auto Playwright:

• Steep learning curve and need for coding skills. This is a code-based tool that requires knowledge of JavaScript or TypeScript. It will also take time to learn if you’re not familiar with Playwright.
• Manual setup. Before starting testing, users must manually configure the testing environment. This can be a complex task.
• Limited test management capabilities. To manage test cases and generate advanced reports, integration with third-party tools is required.

Both tools are powerful solutions for web application test automation. They allow teams to adapt to the dynamic nature of web development and demonstrate excellent results. When choosing a platform, consider the limitations of each. This will help meet your specific testing needs.

Bottom Line

Playwright with AI emerged in response to the need for reliable and efficient web application testing by QA teams. This approach combines the reliable functionality of the Playwright automation library with the advanced capabilities of artificial intelligence.

Testers interested in the rapid creation of tests can use the built-in Playwright Codegen, optimizing its capabilities with ChatGPT and other similar systems. Additionally, third-party AI-based tools are at their disposal.

— Which option to choose We hope our article has helped you make a decision. If you still have questions, reach out to a testomat.io manager for expert consultation on any aspect of testing.

The post Playwright AI Revolution in Test Automation appeared first on testomat.io.