In this article, our seasoned Automation QA Engineer & AI Specialist – Vitalii Mykhailiuk has addressed these questions. Let’s break down the differences between ChatGPT, the general-purpose AI, and Testomat.io, the test management powerhouse built for QA pros like you.

General-Purpose AI: ChatGPT Workflow

ChatGPT, as a conversational AI, excels in free-form reasoning, document analysis, and ideation. So, how a QA engineer might implement ChatGPT in a testing workflow.

Step 1: Requirement Analysis via Prompting

The typical workflow starts by copying raw requirements (PRDs, Jira tickets, or Confluence documentation) and pasting them into ChatGPT. A structured prompt might look like

“Generate test cases for [“Todo list app – create todo feature”] based on the available Jira requirements. Include positive scenarios, negative scenarios, boundary conditions, and edge cases. Results should be in the table format with “Test Title”, “Description”, “Expected Results”.”

The answer we have received:

While effective, this method has technical limitations:

• Prompt engineering overhead: Writing effective prompts is a non-trivial task requiring prompt templating, prompt chaining, and result validation.
• Non automation process: Copy/Past requirements and project data to the well-structured prompt which can take a lot of time.
• Data entry risk: Copy-pasting requirements may omit metadata, links, or cross-references.

Step 2: Test Case Generation

ChatGPT can generate well-structured test cases, but aligning them to internal templates (e.g., title, preconditions, steps, expected results, tags) requires additional prompting:

“Generate “TODO App – create todo feature” well-structured test case text for the title field validation, which has the following logic: if the field is empty (0 characters), an inline error message ‘Title is required’ should appear. Please produce test cases similar to existing ones, considering the validation rules and user interactions. # Steps Identify Valid Conditions: Ensure there are test cases where the title field is voluntarily left blank to trigger the ‘Title is required’ message. – Verify the appearance of the error message when the field is submitted empty. # Output Format Provide test cases in a structured table format with columns “Test Title”, “Description”, “Preconditions”, “Steps”, “Expected results”, “Test notes””

 

Challenges here include:

• Manual data injection: Test data variables must be manually defined and scoped.
• Template adherence: Slight variations in phrasing may break downstream parsing pipelines.

Step 3: Execution Metrics and Test Data Analysis

Analyzing past execution data via ChatGPT requires exporting results (CSV, JSON, or XML) from your test management system and generate a stability report:

“Analyze “TODO App – create todo feature” feature Test Case data and generate a stability feature report:

1. Use available test labels to group tests by functional area or component.

2. Identify tests with possible consistent failures, flaky behavior, or no recent executions….”

Limitations:

• No direct integration: Requires manual data export/import.
• Trend history blindspot: Without access to past executions or historical baseline data, ChatGPT’s insights are limited to the immediate snapshot.
• No test entity awareness: It cannot infer relationships between test suites, execution runs, or code changes unless explicitly encoded.

Built-in AI in Test Management Tools: Testomat.io Approach

The best AI platforms for QA, like Testomat.io, integrate AI directly into the QA lifecycle. Unlike ChatGPT, they operate with full access to internal test data models, suite hierarchies, project metrics, and version history — enabling context-aware and sequence-aware automation.

Inner AI Integration – How Testomat.io Solves Existing QA Problems Technically

Instead of relying on prompt-based instructions, Testomat.io’s AI:

• Parses linked Jira stories or requirements from integrations.
• Automatically maps them to existing test cases or flags gaps.
• Suggests test suites based on requirement diffing using semantic embeddings.
• Pay attention to the custom user’s rules or templates which are used as project general points.

All of this is done by the system “under the hood” and uses project information to generate well-described prompts to get the most accurate information possible.

Auto-generation of Test Suites & Test Cases

With domain-aware generation:

• Testomat.io generates tests directly from requirement objects.
• The AI understands project templates, reusable steps, variables, and even tags.
• It ensures conformity to predefined schema and applies internal templates or rules.

Prompt Engineering & Data Preprocessing in Action

At Testomat.io, we believe true AI integration is about understanding your data. Our platform uses advanced prompt engineering, grounded in your real test management data: including test templates, reusable components, and historical test coverage, to auto-generate test suites, test cases, and even test plan suggestions. This ensures accurate, schema-conforming test generation.

How does it work?

Thanks to our access to comprehensive test artifacts, project settings, and example cases, the system constructs structured prompt templates enriched with real data, functional expectations, and even team-specific conventions. These templates include rules, formatting expectations, and embedded examples, effectively guiding the AI to produce output that is production-ready.

If the response deviates from expected structure, a validation layer flags inconsistencies and requests regeneration or manual refinement to meet the required format, ensuring every generated test is useful and compliant by design.

chatGPT prompt example

<task>Improve the current **test title** for clarity and technical tone.</task>
<context>
	Test Title: `#{system.test.title}`
	Test Suite: 
            “””
            <%= system.suite.text %> (as a XML based content section)
            “””
            …
</context>

<rules>
	* Focus only on the **test title**, ignore implementation steps.
	* Avoid phrases like "make it better".
	…
</rules>

Conclusion

While ChatGPT provides a powerful, flexible assistant for ad-hoc QA tasks, it lacks deep integration with test management artifacts and historical context. In contrast, AI-powered platforms like Testomat.io embed intelligence into the workflow, enabling seamless automation, traceability, and data consistency across the QA lifecycle.

If your goal is full-lifecycle automation, continuous test optimization, an AI-native test management system offers a more scalable and technically robust solution than standalone AI chatbots.

Stay tuned for our next technical article on how Testomat.io’s internal AI pipeline is architected from data ingestion, through LLM integration, to real-time feedback loops.

The post ChatGPT vs. AI Test Management Platforms: What’s Better for QA Documentation Analysis? appeared first on testomat.io.

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.

Thus, poorly developed software products can ruin the user experience and the company’s name and reputation.

The new trend of applying AI agents in testing can minimize human errors, make the QA teams more productive, and dramatically increase test coverage. In the article below, you can find out what AI-based agent testing is, discover types of AI agents in software testing and their core components, learn how to test with AI agents, and much more.

What is an AI Agent?

An artificial intelligence agent, or AI agent for short, is both a small program and a complex system which has been programmed to utilize artificial intelligence technology for completing tasks and meeting user needs. Thanks to its ability to demonstrate logical reasoning and understand context, an AI-backed assistant can make decisions, learn, and respond to changes on the fly. In most cases, AI agents or AI-powered assistants are characterized by the following:

• They can carry out repetitive or expert tasks and can even replace the whole QA team department.
• They can function autonomously when there is a need to attain defined goals (often without constant human intervention).
• They can be fully integrated into organizational workflows.

What is an AI Agent Testing?

When we talk about AI testing agents or assistants, we mean smart systems that apply artificial intelligence (AI) and machine learning (ML) to perform or assist in software testing tasks.

They replicate the work of human testers, including test creation, execution, and maintenance, with limited manual involvement of the specialists, operating under specific parameters they define. It is helpful to have AI-powered assistants in the following situations:

• With their help, anyone in the team, even without technical expertise, can create and maintain stable test scripts in plain English.
• They can automatically adjust, fix, and update tests in terms of the system changes, requiring less effort from human testers.
• Suggest how to make your tests better.
• They can automatically run test cases, which have been created with manual effort, and require minimal direct control of the QA specialists.

Types of AI Agents in Software Testing

Below, you can find information about widely known categories of AI agents in software testing, based on their roles and capabilities:

Simple Reflex QA Agent

Reflex Agents use if-then rules or pattern recognition. They follow specified instructions or fixed parameters with rule-based logic and base their decisions on current information. As the most basic type, these AI agents perform tasks based on direct responses to environmental conditions, which include diverse OSs and web browsers, network connections, structured and unstructured data formats, poorly documented APIs, and user traffic. For example, a task for a simple reflex agent can be to detect basic failures (e.g., 404s, missing elements), log errors, and take screenshots when it detects an error message on the screen.

Model-based Reflex Test Agent

These agents are intelligent enough to act on new data, not just directly, but with an understanding of the context in which it is presented. They are considering the broader context and then responding to new inputs. Can simulate user flows or business logic. Reflex Agents are used in testing to perform more complex testing tasks, because their decisions are based on what they remember/know about the situation around them.

For example, this type of agent remembers past login attempts. If users make multiple failures, it will try to attempt a Forgot Password process or alert about a potential account lockout instead of just logging an error.

Goal-based Agents

When we talk about this type of agent, we also mean rule-based agents. It happens because they follow a set of rules to achieve their concrete goals. They choose the best tactics or strategy and can use search and planning algorithms, which help them achieve what they want. For example, if an agent’s goal is to find every unique error or warning, it can be done by creating test scripts, which will help identify only unforeseen errors and decrease re-testing efforts.

Utility-based AI testing Agent

These agents are capable of making informed decisions. They can analyze complex issues and select the most effective options for the actions. When predicting what might happen for each action, they rate how good or useful each possible outcome is and finally choose the action most likely to give the best result.

For example, instead of just running all tests like Goal-based agents or reacting to immediate errors like simple reflex agents, these ones include a utility function, which allows them to rate situations and skip some less critical ones to find high-severity bugs.

Learning Agents

These assistants can use past experiences and learn from past mistakes or code updates. Based on feedback and data, they can adapt and improve themselves over time. For example, QA teams can use learning agents if there is a need to optimize regression testing. AI-powered assistants self-learn over time from previous bugs and codebase changes, prioritising areas with frequent failures — they highlight the team’s attention on what matters most.

The core components of AI agent for QA Testing

The essential parts of an AI agent for QA testing allow it to intelligently analyse, learn, adapt, and act during the software testing process. Let’s explore them more:

• Perception (Input Layer). Collects data from the environment. It might be code changes, test results, test execution logs, test diff analysis, API responses or some patterns in the test project.
• Knowledge Base. Stores the historical information the agent learns from. Its contents typically include past bugs and their root causes, test coverage data, and frequently failing components, e.g., this helps the Testing Agent make informed decisions based on context and experience.
• Reasoning Engine (AI agent brain). Makes decisions based on current input and the knowledge scope. AI agents use techniques such as rule-based logic, impact analysis, risk-based prioritization, and dependency graph evaluation to process information. For example, the agent can decide which tests to execute based on recent code changes.
• Learning Module. AI agents learn from user stories and test cases by training, adapting to patterns in them over time. This continuous learning helps reduce false positives and minimize test noise, improving overall test reliability. By leveraging natural language processing (NLP), they can predict potential test failures, detect flakiness and intelligently optimize the sequence in which tests are executed.
• Action(Execution Engine). Performs tasks based on decisions. For example: generating new test cases, selecting and proposing the execution of the most relevant tests, reporting defects or opening issues.
• Feedback Loop. Analyzes test engineers’ feedback on false positives to improve future answers. It enables continuous learning and self-improvement of the AI agent.
• Integration Layer. This layer enables seamless connection between the agent and external tools, allowing it to both send and receive data. The incoming information supports the agent’s perception and learning modules, enhancing its ability to respond in a reasoned and intelligent manner throughout the testing process. Integrates the agent with test frameworks, bug trackers, project management tools like Jira, documentation extensions such as Confluence and CI\CD pipelines.

So, these components work together to support automated, efficient, and smarter testing.

Considering this flow… How do you think? Where should test engineers focus the most? Right! It is their prompting and the filling of artifacts and documentation. Moving forward with our next paragraph:

What kind of feeding AI agent is dependent?

First, focus on the efficiency of your inputs — specifically, the prompts— since the accurate and useful results you get will directly depend on how your instructions are formulated. Today, prompting has become a key skill for modern QA engineers working alongside AI-driven tools. Thus,

What is prompt engineering in software testing?

It is your specific questions, instructions, and inputs given to the MCP AI agents to guide their responses; they can generate and improve tests, highlight potential faults, assist with analysis, and decision-making.

5 basic rules for prompting in software testing

1. Be clear, avoid vague instructions. Define exactly what you want: test type, tool, scenario, outcome.

Example of right prompt to generate test cases:

 Generate test cases for the login form with email and password fields and a button Sign In to verify their work.
 Instead of just: Write a test for the form.

2. Provide Context. The more context, the better the result. Include the user story, functionality, code snippet, or bug description.

Example a prompt based on user story:

Based on this user story <User Story RM-1523>: As a user, I want to reset my password via email, suggest edge cases for testing.
Generate tests for password.

3. Include expected behavior or test goals. Helps AI understand the validation points or test criteria. Also, use terms like: edge cases, negative testing or happy path, e.g., to guide the logic.

Example:

Write a test case that ensures users are redirected to the dashboard after logging in.”

4. Avoid overloading the prompt. Do not cram too many instructions in one sentence. It may confuse the MCP Agent’s model and reduce output quality. Better break large tasks into smaller steps, using step-by-step or follow-up prompts.

Example of step-by-step prompt

Start with: “What should I test in…” then ask for detailed test cases or code.

5. Mention tool, framework and format. If you are using a specific stack (like Playwright, Cypress, Selenium), need output checklist code to describe a test case with Gherkin, etc., just say it in the prompt.

Examples:

Write Gherkin-style scenarios for login functionality in Playwright with invalid credentials.
Return the test cases in a markdown table format.

Reasonable artifacts in input data equal efficient AI prompting

Secondly, always remember to keep your artifacts well-structured. Avoid inflating your test project with excessive, unused test cases — that’s a red flag. Prioritize only what’s necessary. This discipline is especially important for AI agents, as they rely on clear, relevant artefacts and operate them in the following:

• Requirements & Specifications. AI agents should have access to a detailed description of the intended purpose and environment for the system under testing. Knowing functional and non-functional requirements allows assistants to better understand what the system should do and how well it should perform in terms of speed, usability, security, etc.
• Existing user stories. AI agents need access to users’ stories to investigate the desired features from a user’s perspective, which allows them to apply them to simulate realistic user journeys and test the end-to-end experience.
• Test Cases. AI-based agents need access to test cases because these give them an understanding of what steps to take and what situations to test when checking software. With this information, assistants can make sure that the software works correctly and can find any bugs.
• Bug Reports. You can find them in Jira, Bugzilla, Linear, or some internal analytics dashboards, as well as external tools like testomat.io Defect Tracking Metrics. AI agents use linking with them for bug reproduction, identifying the fault reason in runs. AI can summarize bug trends to inform QA decision-making in bug prevention.
• Reporting and Analytics metrics. AI agents collect data from tools like Allure reports, CI/CD pipelines, and test dashboards. The AI evaluates test duration, failure trends, and pass/fail consistency. Frequent or critical test failures are flagged for priority investigation. AI agent provides suggestions for fixing unstable tests. Also learns which tests are most valuable for regression based on historical value. Based on these insights, it recommends test automation optimization.
• Documentation. With access to the testing documents, AI agents are in the know of what the software should do and what its goals are. These documents also tell them exactly what to test, give clear rules, and expected results – passed or failed tests.  Also, AI-based agents can run existing tests and learn from past results in reports to carry out smarter and more effective testing.

Choose the best Artificial Intelligence testing agent

The test management system testomat.io  is a modern AI-powered test management tool that helps you easily develop test artifacts and organize test projects with maximum clarity.
It is more than just a data repository and a tool for storing your test artifacts; it offers powerful, AI-driven functionality to accelerate your QA process. The AI Orchestration is integrated across your entire test lifecycle — from requirements to execution and defect tracking — while synchronising automation and manual testing efforts, supported by numerous integrations and AI enhancements.

Other strengths are Collaboration and Scalability. QAs in the team can easily share their test result reviews, flexibly select tests for test plans and runs, adapting AI suggestions to fit their specific needs.

AI Assistant testing works at the level:

Generative AI and Chat with test modes provide direct interaction with the test case suites using natural language — it looks like chatting with a QA teammate. You can generate new test cases or refactor existing ones by automating these repetitive test tasks; manage suggestions, map tests to requirements, identify test gaps and flaky scenarios, and gain a clearer understanding of your test coverage to improve it continuously.

For instance, prompt samples of questions:

→ What does this test case do?
→ Write edge tests for password reset
→ Rewrite this test to be more readable
→ Which parts of the app are under-tested?
→ Find duplicates
→ Map these test cases to requirements
→ To improve desired feedback, teach the MCP AI model by clarifying follow-ups gradually.

AI agent is an intelligent automation component that serves as a bridge between the test management system and the test automation framework ecosystem. This AI agent actively learns, analyzes, and optimizes the testing process. Namely, it can suggest clear, easy-to-understand test descriptions for automated tests — making them accessible even to non-technical stakeholders (Manual QAs, Business Analysts) — and can automatically transform your project into Behavior-Driven Development (BDD) format. Additionally, it detects flaky or failing tests based on execution history.

AI Reporting and Analytics is also our strength. Insights from AI Assistant are not hidden — they are delivered through suggestions in the UI of the test Report. Now the development team has implemented two kinds of AI extensions inside the Report — Project Status Report and Project Run Status Report. These reports are available automatically based on recent test run history. They deliver instant visibility into the health of the test project without delving into individual Test Archive logs.

The AI testing agent provided by testomat.io is an intelligent testing co-pilot. It empowers testers to move faster, test smarter, and reduce risk — all with less manual effort. Below, we break down its capabilities in action and show how its workflow acts.

The Use of AI Agents for Software Testing

AI-driven agents are changing the way software QA engineering teams do their work, enabling them to make the testing process faster, more reliable, and more efficient. Here are the key areas where AI assistants are considered the most reliable helping hand:

Test Case Generation

To speed up the process of creating different test suites, QA and testing teams can use artificial intelligence assistants, which take into account the software requirements and are able to turn simple instructions into test scripts on the fly. Based on Natural Language Processing (NLP) and Generative AI, this process happens much faster and covers a wide range of situations, which would have taken much longer if established with human QAs and testers.

Test Case Prioritization

AI-powered assistants analyze previous test results, code changes, and defect patterns, which help them decide the most effective sequence of test runs. Instead of relying on fixed or random order, these models use data from prior test executions to prioritize tests and optimize the selection of test cases.

Automated Test Execution

AI-based assistants/agents are capable of running tests without QA specialists’ involvement 24/7. When the source code is changed, test suites are automatically triggered to execute continuous testing and provide fast feedback. In addition to that, integrations with test case management systems allow bugs to be reported and all updates to be automatically shared with relevant teams and stakeholders.

Shift-Left Testing

In shift-left testing, AI-based agents deliver faster execution and identify bugs quickly, which enables developers to resolve issues earlier. AI-powered agents can also adapt to evolving project requirements to suggest relevant tests to run based on code changes.

Test Adaptation

Thanks to self-healing capabilities, AI agents can respond to changes in the application’s interface and adjust their actions based on what has been changed.  They can handle UI, API modifications, or backend changes while maintaining automated tests whenever there’s a change in the codebase.

Self-Learning

Thanks to AI agents’ ability to learn from previous findings from tests, they can analyze trends and patterns from past testing cycles, which helps them predict future test results. When learning and adapting, assistants are getting better at identifying potential bugs and making decisions in a jiffy to proactively address them.

Visual Testing

Backed with computer vision, agents can detect UI mismatches across various devices and screen sizes. They verify the aesthetic accuracy of the visible parts that users interact with. AI-based agents aim to find visual ‘bugs’  – misaligned buttons, overlaid visuals (images, texts), partially visible elements, which might be missed during traditional functional testing.

Test Result Analysis

AI agents can review test results on their own in order to find failures and group similar defects. They also point out patterns in the data, which help them detect the root cause faster and focus on what matters most – identifying patterns that may lead to vulnerabilities in the system.

 Overview: Pros and Cons of AI agent for software testing

This table compares the advantages and disadvantages of using an AI agent testing (agentic testing), reminds us about common AI hallucination troubles, and provides a balanced view of AI’s role in testing processes.

Pros of agentic testing	Cons of AI agent testing
Generating test cases that humans might miss. Improve test coverage.	Inability to understand wider context – user intent, business logic, and non-functional requirements that a human tester would comprehend.
Updating the test cases automatically in terms of changes in the code.	Generating test cases, which trigger false positives or false negatives, and requiring careful review before implementation.
Running test suites faster, accelerating the release cycle, and reducing manual effort.	Requiring ongoing maintenance and updates to adapt to evolving testing needs.
Predicting potential bugs based on previous test data.	Having blind spots or ending up with inaccurate predictions when training data is poor and does not cover a broad range of test scenarios and edge cases.
Identifying and fixing broken tests.	Lack of human intuition in complex scenarios.
Self-learning capabilities and adapting testing strategies or techniques based on feedback.	Over-reliance on AI could decrease critical human oversight among test engineers, especially when risks would have appeared in senior and QA manager roles.

How to Test with AI Agents: Basic AI Workflow

When it comes to the entire testing process of the software products, it is essential to mention that AI agentic workflows are Agile process and go beyond simple handling of repetitive tasks.  QA teams should define roles, decide what to test, and what AI agent tool to use.

*It is a plain AI Agent testing Workflow Example; the more sophisticated one was published in a LinkedIn post.  Follow the link to check this AI Agent testing Workflow within testomat.io test management software.

1. Data-Gathering. To get started, AI test agent gather data from many different sources, like APIs, user commands, requirements, past bugs, usage logs, external tools, environmental feedback, and so on, to be trained (if there is a need). Our test management solution supports native integration with many.
2. Collection & Coordination. It is a Role of the Test Management System. Once all the relevant datasets have been collected, the AI-based agents can create relevant test cases to achieve good test coverage, which even covers edge cases, while human testers should approve whether the generated test cases are relevant. Also, AI-powered assistants generate enormous unique test data and user data for email, name, contact number, address, etc., which mirrors the actual real-world data. But when you integrate large language models (LLMs) and generative AI (GenAI), QA agents can rapidly simulate diverse real-world conditions and evaluate applications with greater intelligence and adaptability.
3. Test Execution. AI-powered agents are deployed to autonomously run tests and simulate user interactions to test UI components, assessing functionality, usability, and application performance.
4. Real-time Bug Detection & Reporting. AI-based assistants detect anomalies, frequent error points within the system, and can predict bugs and automatically report defects to stakeholders. In addition to that, it can recognize repetitive flows and high-priority areas for testing.
5. Test Analysis & Continuous Learning. As the software scales, the AI-powered assistants analyse data from user interactions and system updates to keep tests aligned with the application’s current state.
6. Feedback and Improvement. QA team members need to regularly review AI-generated results to maintain software quality. Despite the power of artificial intelligence, it’s important to mention that continuous monitoring and periodic checks of their work guarantee accurate and reliable testing results.

Challenges in AI agent for testing

• When the software product becomes more complex, the amount of computing resources needed for AI testing increases exponentially.
• The absence of representative data leads to testing ineffectiveness – AI-based assistants could develop biases and couldn’t meet ethical standards.
• Using outdated APIs and poor documentation presents a huge challenge for the adoption of AI testing.
• AI-generated test cases and results require careful review before implementation.
• In terms of the AI black box problem, it is difficult for QA teams to understand the logic behind the failure of test cases.

Best Practices AI agent testing implementation

When choosing a test assistant, you need to find the tool that best adapts to your testing needs with the software development lifecycle. Take into account customization, integration, and user-friendliness. However, let us remind!

Do not forget about combining AI and human efforts to balance efficiency and creativity!

Here you can reveal some other tips to help you find the right AI agent testing tool:

1. You need to investigate how your organization is structured, what systems and tools you already use, and what testing tasks you have.
2. You need to define what areas the AI agent testing framework will help you automate before scaling.
3. You need to make sure that your team understands why they need an QA agent in test automation and knows how to use it effectively.
4. You need to discover if an AI bot can be integrated with the platforms you already use.
5. When planning your tool budget, you should consider free, subscription, or enterprise pricing.
6. You need to consider its customisation capabilities to be tailored to your unique testing requirements.

Boost your capabilities with AI Agent Testing right now

Whether you’re a QA lead or a startup founder, applying AI Testing Agents will change the way you carry out testing. They are becoming essential tools for modern QA teams, which can learn from past data and predict failure points. They can also generate different tests, self-heal, and adapt to changes to achieve a higher level of excellence for software delivery in record time.

Are you ready? Contact us to learn more information on how to use the power of Artificial Intelligence agents to create precise test cases and improve the quality and coverage of your software testing efforts.

The post AI Agent Testing: Level Up Your QA Process appeared first on testomat.io.

Did you know that GenAI-based tools will write 70% of software tests by 2028, based on IDC, and the AI use in software testing helps reduce test design and execution efforts by 30% according to Capgemini?

This article dives into what AI in software testing is and why to use artificial intelligence in QA testing – and offers actionable tips to level up your entire testing process and be in sync with the current AI software engineering practices.

Role AI In Software Testing

Artificial intelligence and machine learning algorithms in software testing enhance all stages of the Software Testing Life Cycle (STLC). The adoption of AI in Quality Assurance continues to rise because it boosts productivity while automating processes and enhancing test accuracy.

Topics handpicked for you

• AI Model Testing
• AI Unit Testing: A Detailed Guide
• Role AI testing, in particular, ChatGPT in Software testing

The traditional testing approach depends on manual test script development, while an AI-powered system learns from data to generate intelligent decisions.

Knowing that, AI test automation tools are a good fit for identifying critical code areas, generating test case recommendations, and automatically developing test cases. What’s more, these tools adapt to user interface modifications without requiring regular updates and maintain their ability to detect and test interface elements even when buttons move or their labels change. This is relevant when considering visual testing codeless tools.

The current software testing industry heavily depends on AI to speed up operations while improving test quality. The artificial intelligence system helps create tests and run them while analyzing results and adapting to new changes through learned knowledge.

Only by opting for AI in software testing can you enhance testing speed, provide smarter and scalable solutions, and decrease the need for test maintenance while speeding up testing operations. When integrated, teams achieve faster software releases with increased confidence through their work and the efforts they put in alongside the artificial intelligence capabilities.

What is AI Testing?

When we talk about AI testing, we mean the use of Artificial Intelligence(AI) and Machine Learning(ML) technologies in the testing process, which helps improve its speed, accuracy, and efficiency. Both are becoming essential in modern QA strategies.

In contrast to classical testing, applying AI-based approaches promotes intelligent analysis and processing of testing data and previous test cycles, fosters test case selection and test case prioritization, and offers the detection of UI inconsistencies and much more. Smart software testing solutions, like predictive analytics, pattern recognition, and self-healing scripts — improve overall software quality.

Manual Software Testing VS AI in Software Testing

It is not a secret that conducting the traditional software testing process requires significant time and efforts, which makes QA and testing teams manually design test cases, make updates after recent code changes, or inadequately simulate real user interactions.

Of course, they can create automated scripts for some components where it is possible, but they also require continuous adjustments. AI in software testing has changed the situation and made it more reliable, efficient, and effective (of course, when following the right approach and using the right AI testing tools).

Thanks to it, teams can automate many repetitive and mundane tasks without risk, more accurately identify and predict software defects, and speed up the test cycle times while improving the quality of their products. Furthermore, it helps them make adjustments before deployment and predict areas, which are likely to fail, reducing the chances of human error and overlooked issues.

Summary

Manual testing focuses on human insight and intuition, while AI in testing brings speed, adaptability, and data-driven intelligence to the QA process.

When to Use What?

→ Manual Testing: Best for exploratory testing, usability evaluation, edge cases, or when AI setup is not justified.
→ AI Testing: Ideal for repetitive tasks, large-scale regression, risk prediction, and accelerating Agile, CI\CD workflows.

Let’s dive deeper into the use cases of how AI is used in real testing workflows.

Current Landscape: How to Use AI in Software Testing?

So, you can find popular use cases to apply generative AI in software testing below:

Test generation & Accelerated testing

Testers face a long and tiresome process of creating test scenarios. Thanks to gen AI in software testing, this process has changed. Now, AI-based tools can be applied for the generation of tests.

They analyze your codebase, requirements, user acceptance criteria, and past bugs to automatically create new tests, which will cover a wide array of test scenarios and detect edge cases that human testers might miss, and accelerate the testing process.

Low-code testing | No-code testing

The combination of Low/No-code testing with artificial intelligence allows testing teams to create and execute tests quickly and reduce the need for human resources and time. Even non-technical team members can actively participate in test automation and faster feedback loops, which contribute to more stable software releases.

Test data generation

With AI test data generation, QA teams can get new data that mimics aspects of an original real-world dataset to test applications, develop features, and even train ML/AI models. It helps them achieve better test results, AI model predictability and performance.

AI can automate the generation of test data in several ways:

→ To create datasets that cover a wide range of scenarios, user behaviors and vary inputs.
→ To produce anonymized data with key features, without personal identifiable information.
→ To generate test data that closely reproduces user actions and situations.
→ To create data for rare and complex testing scenarios which are difficult to capture with real-world data alone.

Test report generation

Artificial intelligence reduces time on manual report creation. With AI-based algorithms, you can automate various aspects of report creation and quickly build test reports which help teams in the following situations:

• Investigate the failure reasons after the completion of tests.
• Visualize test results and provide the visual indicators of test performance.
• Configure simple and understandable reports for your teams
• Analyze the roots of failure and suggest possible solutions for resolution.

Bug Analysis & Predictive Defects

Based on past test data and pattern recognition, AI-based tools can predict which line of code is likely to fail. This helps testers concentrate their efforts on high-risk areas to boost the chances of detecting defects early in the automation testing process. Thanks to predictive defect analytics, test case prioritization, and bug identification are getting more efficient and quicker in the testing process.

Risk-based testing

Risk-based testing focuses on areas that pose the greatest risk to the business and the user experience. With AI-based tools, teams can reveal the “risk score” for each feature or workflow and increase test coverage for them. AI helps them prioritize testing efforts based on potential risks and balance the use of resources, concentrating on areas with the greatest potential impact. More information about risk-based testing can be found here.

Why Teams Need AI in Software Test Automation

Without fear of oversimplifying, the biggest challenge that testing teams face is automating repetitive testing tasks that require a lot of time to perform. However, AI in testing is that not only solves this key problem. In fact, it can handle other, no less pressing issues. Let’s discover why teams need AI in software test automation:

• Teams need Artificial intelligence to automate similar workflows and orchestrate the testing process.
• Teams need Artificial intelligence to highlight which test cases to execute after changes in the program code of some features to make sure that nothing will break in the app before its release.
• Teams need Artificial intelligence to know what test scripts to update after changes in the UI.
• Teams need Artificial intelligence to know what feature or functionality requires immediate attention.
• Teams need Artificial intelligence to expand test coverage by revealing edge cases and allocate testing resources efficiently.
• Teams need Artificial intelligence to reduce delays in regression testing and find possibilities to speed up testing.

However, it is important to mention that artificial intelligence in testing cannot deal with situations not included in the training data or replace human judgment.

AI in Software Testing Life Cycle

Artificial intelligence can be integrated into the key stages of the testing lifecycle – planning, design, execution, and optimisation. Below, you can find more information about each stage:

#1: Test Planning

With artificial intelligence, the requirement documentation, user stories, and specifications for the testing process can be processed in seconds. Depending on the information that AI will gather from them, it can convert them into testable scenarios.

When implementing this approach, teams can remove the possibility of errors during the test creation process and reduce the manual efforts, which are required to analyze large documentations and identify inconsistencies at the earlier phases of the development cycle.

Also, AI-based algorithms can go through the historical project data and predict high-risk areas of the applications that are more prone to failure and repurpose all the testing efforts accordingly.

#2: Test Design

Using AI, teams can automatically create the tests depending on the requirements and user behaviors, and suggest areas of the application that require further testing. With accurate and varied test data, teams can also ensure that all the tests that occur in real-world scenarios are met. Artificial intelligence will also collect that data to do variability and compliance testing around GDPR, so you are compliant with user privacy and security.

#3: Test Execution

AI’s goal is to minimize the time required for test execution and improve real-time decision-making about testing strategy. Teams can integrate AI to create AI-driven tests that automatically find the UI changes and update the locators within the tests, which leads to the scalability of tests and their dependability as well. Furthermore, teams can apply AI to understand the optimal execution strategy – they are in the know which tests to run and on which platform/environment, taking into consideration previous testing results and current changes within the application infrastructure.

#4: Smart bug triaging

If bugs are not recorded, mapped, and reported properly, then the time and efforts involved in identifying the root cause and rectifying them are much higher. Thanks to AI-based natural language processing techniques, teams can intelligently address bug triage.

Artificial intelligence can automate the creation, update, and follow-up of bug reports and get a full picture of your tests’ performance. By spotting flaky tests and using historical data, it identifies the best tests for the task instead of wasting resources on unnecessary testing.

#5: Self-healing tests

Traditional automated testing requires extensive time to maintain scripts because of UI updates and functional changes. The testing scripts frequently fail and need human intervention for updates when working in environments with dynamic development. AI-based algorithms can be utilized for autonomous issue detection, precise test case generation, and software change adaptation without requiring human involvement.

#6: Test Reporting

With AI-powered reporting, testing teams can generate detailed and actionable testing dashboards with detailed information and recommendations using AI. It also speeds up the defect triaging and helps teams define the resolution strategy to get rid of bugs in less time before the software system is deployed. In the long run, it improves the visibility across multiple teams and enables them to make faster decisions to reduce the feedback loops and also the production cycle.

#7: Test Execution Optimization (Maintenance)

AI-powered systems learned from past executions and user interfaces help teams identify the flaky or low-value tests and give recommendations – whether to remove or refactor them in order to meet the requirements. Thanks to AI, teams can find failures and link them back to the code changes, infrastructure issues, or integration errors, and minimize the overall troubleshooting steps.

Test Management AI Solves Software Testing Extra Tasks

Flaky test detection

When your test suite grows, flaky tests become a common problem for many development and QA teams. If left unchecked, they lead to false positives (tests that pass but shouldn’t) or false negatives (tests that fail but shouldn’t). Thanks to AI-based tools, teams can identify and score flaky tests, and then define which tests to re-run or skip and which ones mean the code needs fixing.

Code coverage analysis

In testing, code coverage quantifies how much of the source code is exercised by the test suite running. Teams can also measure what percentage of code is executed during those tests and understand how effective the testing strategy is.

If code coverage is high, it indicates that a larger portion of the code has been tested under various conditions. With the integration of AI, teams can get a full coverage review of code, study the app code thoroughly, and suggest tests achieving over 95% test coverage. It prevents the likelihood of any defects escaping into production due to insufficient tests.

Regression automation

With AI-based regression testing tools, teams can adapt to changes in scripts and prioritize tests. Artificial intelligence can manage large numbers of regression tests by automatically detecting changes and identifying areas that are likely to be most affected by new updates. By analyzing defect patterns, user behavior, and historical data, Artificial intelligence helps identify risk-prone areas and provides thorough testing of critical functionalities, saving manual effort and accelerating test cycles.

Test orchestration

With orchestration in place, teams can perform several rounds of testing within an extremely limited amount of time and achieve the desired levels of quality. Thanks to Artificial Intelligence test orchestration, it optimizes the selection of tests and intelligently prioritizes the right ones for execution based on code changes and risk, rather than simply running everything.

With its help, teams can dynamically manage the execution of tests across diverse environments and validate the reports for successes/failures, including the report on smoke testing and performance testing, and configure the right capacity of resources needed.

For example, the AI Testing Assistant from testmat.io can help QA teams make decisions regarding determining the project’s release readiness or assessing its quality.

Benefits of AI in Software Testing

AI brings significant improvements to how software is tested, especially in Agile, Shift-left and fast-paced CI\CD, DevOps, TestOps modern methodologies. Below are the key benefits:

Detailed some benefits incorporating AI:

• Visual AI Verification. With AI, teams can recognize patterns and images that help to detect visual errors in apps through visual testing, which guarantees that all the visual elements work properly.
• Up-To-Date Tests. When the app grows, it changes as well. Thus, tests should also be updated or changed. Instead of spending hours updating broken scripts of tests, Artificial intelligence can automatically adjust tests to fit the latest version of your application.
• Improved Accuracy and Coverage. By scanning large amounts of data, AI finds patterns and highlights areas that require more attention. It also measures how much of the application is tested and reduces the risk of bugs before production.
• Automation of Repetitive Tasks. Artificial intelligence is helpful when it comes to the automation of repetitive tasks and lets teams focus on the things that need human attention, like exploratory testing.
• Faster Execution of Tests. Thanks to AI in software testing, tests can be executed 24/7, which leads to faster feedback and quicker development cycles.
• Reduced Human Error. When teams do manual testing, it can lead to mistakes. AI changes this situation and does the same work without losing focus, and eliminates bugs caused by missed steps or overlooked details.

Challenges of AI in software testing

Below, we are going to explore the challenges of AI in testing that development and QA teams face when implementing it:

• AI is highly dependent on data and requires quality data to be trained on for producing correct and unbiased recommendations.
• Devs and QA teams need to constantly monitor and validate the data generated by AI, because even a small error may break the existing functioning unit tests.
• Devs and QA teams face difficulties in explaining AI-driven decisions and might cope with the risk of biased AI models.
• It is important to mention that AI is not a full replacement for human testers, but a help for them in automating repetitive tasks, speeding up test execution, and improving accuracy.
• AI implementation requires significant initial setup and continuous learning and updates.
• It produces training complexity and is computationally expensive in the initial phase.

Tips for Implementing AI in Software Testing

Below, you can find some information you need to know to successfully implement AI in testing:

Define Goals

To get started with AI implementation, you shouldn’t forget about setting testing goals. All these questions should be asked and answered from the very beginning:

• Do you need to increase test coverage or test execution time?
• Do you need help deciding on software quality or release readiness?
• Do you need to boost bug triaging?

Choose the Right AI Tool

Taking into account your quality assurance objectives, you need to assess project demands and choose an AI tool that fits your needs and development environment. Don’t forget about usability, scalability, and integration capabilities of the right AI test automation frameworks during the selection process.

Prepare High-Quality Training Data

You need to remember that AI testing success depends on training data quality. For the AI to start providing accurate outcomes, it should be trained on quality datasets which go through iterative data refining steps. You need to establish data policies, standards, and metrics that define how data is to be treated at your organization. Also, you shouldn’t forget to implement data audits, which reveal poorly populated fields, data format inconsistencies, duplicated entries, inaccuracies in data, missing data, and outdated entries to make sure the training data remains high quality.

Incorporate Metrics for AI assessment

You need to establish meaningful success criteria and performance benchmarks aligned with real-world expectations for AI in software testing. With statistical methods and metrics, you can measure the reliability of AI model predictions and its results. Also, you can incorporate human judgment for evaluating AI effectiveness.

Continuous Monitoring and Improvement

For better results, you need to continuously analyze AI testing results and find areas for improvement, audit training data, and adjust artificial intelligence parameters to keep AI as efficient and flexible for software testing as possible.

Wrapping up: Are you ready for AI and software testing?

It is crucial to remember that there is no “one-size-fits-all” solution anywhere, even in testing. Before implementing AI for software testing, assessing artificial intelligence readiness in your organisation is essential. All the current testing processes, team capabilities, and specific QA challenges should be investigated.

Furthermore, you need to discover areas of weakness where AI can help, choose the right tool to address them, and then start integrating it into your process. If you need any help with AI in testing software, our team understands the AI life cycle and is equipped with the AI-based tool you need for an effective and fast AI software testing process.

The post AI in Software Testing: Benefits, Use Cases & Tools Explained appeared first on testomat.io.

To adopt AI-model testing, you need to effectively manage the massive amounts of data generated during the testing process. Furthermore, you need to train AI models using these vast datasets and enable the models to make accurate predictions and informed decisions throughout the testing lifecycle.

In practice, the problem of introducing AI-models into a real business is not limited to new data preparation, development, and training. Their quality depends on the verification of datasets, testing, and deployment in a production environment. When adopting the concept of MLOps, QA teams can increase automation, improve the AI-model quality, and increase the speed of model testing and deployment with the help of monitoring, validation, versioning, and retraining.

In the article below, we are going to find out essential information about AI-model testing and its lifecycle, reveal popular tools and frameworks, and explore key strategies and testing methods.

What Is an AI Model?

When we talk about AI-models or artificial intelligence models, we mean mathematical and computational programs which are trained on a collection of datasets to detect specific patterns.

In Simple Terms:

AI model is like a trained brain that learns from data and then uses that knowledge to solve real-world problems.

AI-models follow the rules defined in the algorithms that help them perform tasks from processing simple automated responses to making complex problem-solving. AI models are best at:

Analyzing datasets
Finding patterns
Making predictions
Generating content

What is AI Model Testing?

AI Model Testing is the procedure of testing and examining an AI-model carefully to make sure it functions in accordance with design specifications and requirements. AI model’s actual performance, accuracy, and fairness are also considered during the testing process, as well as the following:

• Whether the predictions of the AI-model are accurate?
• Whether an A-model is reliable in practical circumstances?
• Whether an AI-model makes decisions without bias and with strong security?

Google’s Gemini, OpenAI’s ChatGPT, Amazon’s Alexa, and Google Maps are the most popular examples of ML applications in which AI-powered models are used.

Why Do We Need to Test AI Models?

Below, we have provided some important scenarios why testing an AI-based model is essential:

• To make sure AI-models deliver unbiased results after changes or updates.
• To increase confidence in the model’s performance and avoid data misinterpretation and wrong recommendations.
• To reveal “why” the AI-based models make a particular decision and mitigate the potential negative results of wrong decisions.
• To confirm that the model continues to perform well in real-world conditions in terms of biases or inconsistencies within the training data.
• To deal with scenarios in which models have misaligned objectives.

*AI, as well as APIs, are at the heart of many modern Apps today.

AI Model Testing Methods

Carrying out various testing methods allows teams to make sure the model is accurate, reliable, fair, and ready for real-world use. Below, you can find more information about different testing techniques:

• During dataset validation, teams check whether the data used for training and testing the AI-based model is correct and reliable to prevent learning the wrong things.
• In functional testing, teams verify if the artificial intelligence model performs the tasks correctly and delivers expected results.
• When simultaneously deploying AI-based models with opposing goals, teams opt for integration testing to test how well different components of the ML systems work together.
• Thanks to explainability testing, teams can understand why the model is making specific predictions to make sure it isn’t relying on wrong or irrelevant patterns.
• During performance testing, teams can reveal how well the model performs overall on unseen large datasets and functions in various circumstances.
• With bias and fairness testing, teams examine bias in the machine learning models to prevent discriminatory behavior in sensitive applications.
• In security testing, teams detect gaps and vulnerabilities in their AI-models to make sure they are secure against malicious data manipulation.
• Teams examine whether the model’s performance does not change after any updates with regression testing.
• When carrying out end-to-end testing, teams ensure the AI-based system works as expected once deployed.

AI Model Testing Life Cycle

To get started, you need to identify the problem the AI-model solution will solve. Once the problem is clear, it is essential to gather detailed requirements and define specific goals for the project.

#1: Data Collection and Preparation

At this step, it is important to collect the necessary datasets to train the AI-powered models. You need to make sure that they are clean, representative, and unbiased. Also, you shouldn’t forget to adhere to global data protection laws to guarantee that data collection has been done with privacy and consent in focus. When collecting and preparing data, you should consider key components:

• Data governance policies which promote standardized data collection, guarantee data quality, and maintain compliance with regulatory requirements.
• Data integration which provides AI-models with a unified access to data.
• Data quality assurance which makes sure that high-quality data is a continuous process and involves data cleaning, deduplication, and validation.

#2: Feature Engineering

At this step, you need to transform raw data into features, which are measurable data elements used for analysis and precisely represent the underlying problem for the AI model. By choosing the most relevant pieces of data, you can achieve more accurate predictions for the model and create an effective feature set for model training.

#3: Model Training

At this step, you need to train AI-powered models to perform the defined tasks and provide the most precise predictions. By choosing an appropriate algorithm and setting parameters, you can iteratively train the model with the processed data until it can correctly forecast outcomes using fresh data that it has never seen before. The choice of model and approach is critical and depends on the problem statement, data characteristics, and desired outcomes.

#4: Model Testing

Before the testing step, it is highly recommended to invest in setting up pipelines that allow you to continuously evaluate the chosen model and determine the AI model’s capabilities against predefined performance metrics and real-world expectations. You need to not only examine accuracy but also understand the model’s implications – potential biases, ethical considerations, etc.

#5: Deployment

After the AI model testing step, you can start the deployment of the model by transitioning from a controlled development environment to one that can provide valuable insights, predictions, or automation in practical scenarios. This step involves tasks like:

• Establishing methods for real-time data extraction and processing.
• Determining the storage needs for data and model’s results.
• Configuring APIs, testing tools, and environments to support model operations.
• Setting up cloud or on-premises hardware to facilitate the model’s performance.
• Creating pipelines for ongoing training, continuous deployment, and MLOps to scale the model for more use cases.

#6: Monitoring & Retrain

At the monitoring step, you need to provide ongoing performance evaluation, regular updates, and adaptations to meet evolving requirements and challenges. If done, you can make sure that the AI model functions in real-world conditions effectively, reliably, and in ethical alignment.

The Retrieval-Augmented Generation (RAG) approach uses its project data along with generic industry knowledge. Keep in mind, data quality in model training and testing is crucial to avoid pesticide effects.

Look, as AI Model Testing Life Cycle goes

As we can see in the illustration below, the testing process involving AI is sequential and cyclical. The stage of development and implementation of the AI strategy is major.

AI Testing Strategy: How to Use AI Models for Application Testing

AI is not a magic bullet, but a powerful co-pilot. By integrating AI models into your testing strategy, you can streamline test creation, enhance coverage, predict defects, and even reduce flaky results. These transform your test strategy into a smarter, faster, and more adaptive system. Leveraging artificial intelligence in application testing automates complex tasks.

#1: Identify Test Scope

At the very start, it is essential to define the goals that should be attained with AI model testing. Whether you need to automatically create new test scenarios, detect UI changes, or adapt flaky test scripts.

#2: Select and Train AI Model

Based on your goals, you need to choose an appropriate artificial intelligence model that best meets your software project requirements.

• Generative artificial intelligence models and Large Language Models (LLMs) are used for creating diverse and intelligent test cases.
• Computer Vision Models are a good fit for visual regression testing and UI element recognition.
• Reinforcement Learning Agents are well-versed in simulating complex user journeys.
• Anomaly Detection Algorithms is a good option for identifying deviations in system behavior (for instance, in logs or performance data).
• Predictive Models can be applied if there is a need to prioritize tests or predict defect likelihood.

Once the AI-model has been selected, you need to make sure you have all the necessary data for training: past test cases, test coverage results, UI snapshots/screenshots, software requirements, design documents, and user behavior data. Also, it is important to verify that it performs well.

#3: Integrate AI into the Existing AI Model Testing Framework

Once trained and validated, you should connect the AI-model with your current test automation tools and CI\CD pipelines. You can use custom testing platforms that offer pre-built integrations or automate the data flow between your application, test infrastructure, and the AI model. At this step, you can automate the testing process for generating test cases, analyzing test results, or UI changes for visual regressions.

#4: Analyze and Refine the AI Model

At this step, it is essential to review the AI-driven testing results and validate them. You need to review the test cases suggested by AI and investigate flagged anomalies, because human expertise still remains crucial for decision-making and context. Based on human feedback, you can retrain and improve the artificial intelligence model and adjust its specific goals if the testing needs of your AI application are changed.

#5: Employ MLOps for Retraining and Versioning

If you run several models simultaneously, need a scalable infrastructure, or require frequent AI-model retraining, you can automate deployment and maintenance with MLOps. Without MLOps, even advanced models can lose their value over time due to data drift. By implementing MLOps, or DevOps for machine learning, you can:

• Automate model retraining, deployment, and monitoring processes.
• Accelerate seamless interaction between data scientists, ML engineers, QA engineers, and IT teams.
• Guarantee version control for models, data, and experiments, and provide monitoring and retraining of the models.
• Support scalability and manage multiple models and datasets across environments, even as data and complexity grow.

From data processing and analysis to scalability, tracking, and auditing, when done correctly, MLOps is the most valuable approach, which enables releases to end up a more significant impact on users and better quality of products.

Advantages of AI-based Model Testing

Here are the most important reasons why you should embrace AI model testing:

Challenges to Testing AI-based Models

In testing, QA teams usually face the following challenges:

• Being dependent on data, AI models in testing are as good as the data they are trained on and learn from. If the data is noisy, incomplete, and full of bias, the model will produce incorrect results and give wrong recommendations.
• In comparison to traditional software, AI-based models can’t deliver identical outcomes for the same parameters, especially during training, which makes testing a little bit tricky in terms of the ability to predict or replicate the results.
• When coping with edge cases, AI test models can cause unexpected failures in terms of unusual input data that they have not seen before.
• Complex AI-based models can be Black-boxed and hard to interpret how they make decisions or why they make a certain prediction.
• Testing for bias and fixing it can be difficult in terms of presenting biases in the training data or through the algorithm’s design.
• Training complex models often requires specialized hardware and significant infrastructure investment.
• It can be difficult to set up clear and precise criteria for evaluating the correctness of AI models because of the complexity and nuance of their outputs.
• When testing AI models, you need to make sure they adhere strictly to legal and ethical considerations to avoid trouble after deployment.

Software Testing Tools and AI Model Testing Frameworks

To conduct effective and efficient testing, you need to choose the appropriate tools, and you need to adhere to best practices. Thus, the testing process can be greatly increased with the appropriate AI testing tools, including the following:

Best Practices for Testing AI Models

Here are some best practices to follow to conduct effective AI Model Testing in your organization:

• You need to prepare clean and unbiased data for testing and training AI models.
• You need to automate repeated test scenarios to accelerate the testing process.
• You need to track model performance and conduct fairness and bias tests to maintain its accuracy in real-world applications.
• You need to update models frequently with fresh data and make sure AI model actions can be traced back.
• You need to implement MLOps to automate data preprocessing, model training, deployment, and to keep models updated.

Bottom Line: Struggling with AI model Testing?

Navigating the AI-model testing is a complex but rewarding journey. It requires defined goals, data quality, a well-thought-out MLOps approach, solid technical expertise with ethical considerations from the start, and strategic vision to reduce release lifecycles and iteratively improve the AI products.

Whether you test one model or more, you should focus on automation, collaboration, and continuous monitoring to make sure your models remain accurate and safe. Contact testomat.io if you have any questions, and we can guide you through the AI model testing process to help you address your unique challenges.

The post AI Model Testing Explained: Methods, Challenges & Best Practices appeared first on testomat.io.

What is AI Unit testing?

AI unit testing means using artificial intelligence to automate test case generation of unit tests and data preparation processes. It eliminates manual efforts and verifies the behavior of each unit in isolation. If a unit does not do what it should do, the software program will not work efficiently or will not work at all.

How can artificial intelligence be applied in unit testing?

Here are six ways artificial intelligence can help you carry out unit testing:

#1: Test Case Automation

With AI tools, QA teams can save time and resources by letting machine learning algorithms analyze the lines of code and quickly generate automated test cases. By analyzing both your code and the code segment context, AI can automatically select high-risk areas for testing, generate unit tests for code segments or recommend tests that will provide insights into your code’s behavior. It will reduce manual workload and speed up the testing process.

#2: Test Case Generation

By using AI,  teams can automatically generate a variety of test cases that cover a wide range of scenarios and conditions. Algorithms of generative AI for unit testing properly analyze the code to identify critical points and generate effective test cases, which will cover every possible execution scenario and enable team members to identify potential issues at early stages or before implementation.

#3: Test Case Selection

With AI-based tools, teams can quickly identify the tests which are most likely to uncover defects or choose a subset of test cases from the entire test suite to be executed in a particular testing cycle. Without running the entire suite, the aim is to select those test cases which are most likely to uncover defects.

#4: Test Case Prioritization

By using AI-backed tools, teams can see how tests are prioritized based on the code complexity,  history of bugs, and code changes. By arranging test cases in a sequence that maximizes certain criteria, they help in detecting critical defects early, improving the efficiency and effectiveness of the testing process.

#5: Test Suite Optimization

AI can identify redundant or less effective tests, helping to reduce the overall test execution time. It detects error-prone areas of code and focuses testing efforts on critical flows. Furthermore, it is effective when giving recommendations on the tests that should be performed for greater test coverage.

#6: Automated Test Maintenance

Thanks to AI, test failure logs can be analyzed to identify the root cause of failures. It can also suggest potential fixes to the code or automatically update and repair existing tests to maintain their relevance and effectiveness.

Benefits of using AI to create unit tests

AI-assisted unit test creation comes with several benefits for the QA and development teams:

• Artificial intelligence tools are effective when generating a large number of tests.
• Artificial intelligence tools provide high code coverage across the project by applying the same level of thoroughness to every piece of code.
• Artificial intelligence systems can learn from feedback and improve their unit test generation efficiency over time.
• Artificial intelligence tools identify and test edge cases that eliminate human errors of overlooking.
• Artificial intelligence tools cut down the time developers spend on writing, maintaining, and running tests.
• Artificial intelligence tools update existing test suites in response to changes in the codebase.

Challenges of unit testing with AI

While AI Unit Testing offers numerous benefits, teams may face some challenges. Let’s reveal what they are:

• When it comes to unit testing with AI, teams lack standardized testing frameworks and can not establish consistent testing procedures across projects and teams.
• Teams may face difficulties when dealing with large datasets, and they require more efficient methods to manage and process vast amounts of data during the test execution process.
• When analyzing code syntax and logic, AI lacks the deep contextual understanding and might miss the broader context and business logic that dictate correct functionality, which result in tests that do not fully cover the necessary edge cases or misinterpret the intended functionality of the code.
• It may get harder for developers to rely on test automation to catch real issues. It happens because AI can sometimes generate tests that either falsely pass or fail.

To avoid mistakes, you need to write your tests before you write the actual code so that each part of your application is tested as it’s developed. Also, you need to make sure that you use realistic synthetic data that mimics real-world scenarios before generating tests.

More importantly, you need to integrate unit testing into your CI\CD pipelines to ensure tests are automatically run with every code change, catching bugs early. It helps maintain code quality throughout development.

Popular AI Tools for Unit Testing

Here are the top tools on the market today for writing unit tests. These tools use various AI techniques to automate and optimize different aspects of code review, test generation, and quality assurance.

• CaseIt It is a specialized testing tool that automatically generates test cases for diverse testing scenarios.
• Bito Used for Behavior-Driven Development (BDD), this tool offers artificial intelligence code reviews for Git workflows, AI code generation, and plan-to-production developer agents for IDE or CLI.
• Unit-test.dev This AI tool helps teams create unit test cases, supports multiple languages (Python, JavaScript/TypeScript, Java, C#) and IDEs to produce more accurate results when used in specific parts of the code.
• Virtuoso QA Using natural language processing, it simplifies test creation and execution, provides low-code/no-code testing, self-healing test scripts, etc.
• Checksum.ai This tool applies AI for test creation and maintenance.
• Carbonate Integrated into your existing testing framework, it helps teams write tests in plain English, offers a code coverage analysis, and detects areas lacking proper unit testing.
• Google Cloud’s Duet It offers AI-based code completion and generation for developers.
• Diffblue Cover With this AI-powered tool, teams can automatically generate JUnit tests for Java applications.
• Keploy An AI tool used as a test case generator for end-to-end test cases based on real user interactions.
• Github Copilot It is used to generate unit and integration tests as well as help improve code quality.

One of the coolest tools on this list is Copilot. So let’s take a look at AI unit testing in action with an example of how Copilot works. We will show you how to start using AI Copilot by demonstrating the ins and outs of generating test automation. After that, we’ll discuss Copilot’s strengths and weaknesses. Although many tools listed here use similar NLM concepts, we will not compare them in this context.

Why Copilot?

GitHub Copilot is a reasonable choice for QA engineers and developers; it boosts their productivity, improves code quality, and helps release faster.

GitHub Copilot for AI unit testing helps reduce the tediousness of writing unit tests. Integration into an IDE is advantageous as the testing tool exposes the code to the AI Copilot Chat, making it easy to tell the IDE to generate tests for a function, method, class, etc. Even a junior coder can easily write unit tests to ensure quality development. It has wide support in VS Code, Visual Studio, IntelliJ IDEA, Vim, and other IDEs. Works with multiple programming languages.

Microsoft provides the Copilot feature or service to users at no cost, and charges a premium for its advanced features.

Utilizing Copilot for Writing Unit Tests in VS Code

Copilot offers several ways to generate tests. We are focusing on using the Copilot integration with Visual Studio Code, which is a fairly representative one. To use Copilot in VS Code, we must first install it. Important prerequisites — you must have a GitHub account if you are using Copilot.

After installation, GitHub Copilot displays the chat screen as shown below.

AI-generated Unit Tests with VSCode

In VSCode, there are two primary ways to generate tests. You can enter commands in Copilot chat or you can use the right-click menu in a code file and select to generate tests. It offers AI-based code suggestions and auto-completions. To generate tests in the Copilot chat, enter a prompt asking Copilot to generate tests for the method or function. As a suggestion, on our request, Copilot provides unit test cases.

Occasionally, Copilot responses might introduce errors because it lacks full context and a natural sense of user sense — be sure to double-check the results of its suggestions. And look at an example of Jest Unit tests Copilot provides:

This Jest Unit test Copilot example code does not include setTimeout()which is better than jest.runAllTimers() in our use case. It might cause runtime issues. However, numerous users have found that Copilot attempts to predict your application’s logic but lacks a true understanding of its underlying structure or embedded details. It operates within the confines of a specific code snippet and ultimately functions in a highly intuitive manner.

Test coverage is always lacking in one way or another, if it exists at all. Leveraging AI unit testing in development is a good way to add value and decrease the significant risk of non-qualitative code.

You might also find this topic valuable:

Automated Code Review: How Smart Teams Scale Code Quality

Asking an AI to generate test automation for your code has the added advantage of providing an extra pair of eyes on your code. To an extent, the quality of the generated test code is correlated to the quality of the code being tested. When AI Copilot struggles to generate tests or produces tests, it can be an indication that the code is not easily testable, the application code is complex or incomplete. Conversely, it offers a valuable hint about refactoring: if Copilot struggles to suggest text, it may indicate that your code is overly complex and could benefit from simplification.

GitHub Copilot Agent VS Copilot Chat

You should pay attention to GitHub Copilot Agent. GitHub Copilot Agent is not only a code suggester, it is an advanced AI-powered extension that provides multi-step assistance to teams across the entire software development lifecycle — not just code completion. Learn more with the Execute Automation YouTube video, How GitHub Copilot Agent Writes Perfect Code & Tests

Best Practices For Implementing Unit Testing AI in general

We hope that following these best practices will help you implement AI unit testing successfully:

• At the very start, you need to define the goals you aim to achieve with your AI unit testing and make testing data clean and well-prepared. Removing inconsistencies and errors from your data enhances reliability. This also improves the validity of your unit tests.
• You need to create isolated tests for individual units in isolation. to identify specific issues within each unit and make debugging easier and more effective.
• With artificial intelligence tools for writing unit tests, you can generate tests and data automatically, adapt to changes in the codebase, and continually improve the tests. However, don’t forget to keep your test cases and data up to date, regularly track test coverage, and analyze performance metrics to optimize your testing strategy.
• By updating tests regularly, you make sure that test cases remain relevant and effective in catching new bugs.

Bottom Line: Ready to use AI-based Tools For Unit Testing?

With AI-driven tools for unit testing, you can make sure that your software testing is both efficient and highly effective. You can also ensure that web applications and mobile applications are functional and reliable. By implementing effective testing strategies and utilizing the right AI tools, you can improve code quality, reduce bugs, and avoid delays and bottlenecks in the development cycle.

If you’re hesitant to apply AI directly to a production codebase, that hesitation is well-grounded. Anyway, AI is amazing, as it significantly speeds up work, allowing us to deliver quality products and add more value to our clients in a more timely manner. However, we should always be cautious, keep our eyes open, and ensure we understand what we’re doing and what the AI tools are doing for us.

Drop us a line today to learn how we can help you enhance your testing processes and deliver high-quality software that meets the highest standards.

The post AI Unit Testing: A Detailed Guide appeared first on testomat.io.

This list shows tools that work for qa teams with many needs. You will find some testing tools for test case management, and others for continuous testing. These tools help with test creation, test runs, and test coverage. The tools are ready to fix test management problems at any step. This way, your work goes well, and you can give the best results each time.

What are the top AI test management tools currently available?

The top AI test management tools are Testomat.io, Test.ai, Qase, and Zephyr. These platforms use artificial intelligence to help teams with their test management. They make it easy to keep track of test cases. The tools are made to streamline testing processes and improve the way people work together. They also help teams get better coverage for their software projects. All these features work together to boost how much you get done and help keep quality high.

1. Testomat

Testomat.io leads the way with its AI-powered features, intuitive interface, and strong integration with test automation and CI/CD workflows. Built for modern development teams, it helps automate test case creation, self-heal tests when UI changes occur, and provides actionable reporting for decision-making.

QA teams benefit from a clean, collaborative environment where test execution, requirements traceability, and analytics are tightly connected. Testomat.io also includes powerful integrations with tools like GitHub, GitLab, Jira, and Slack, allowing for real-time notifications and fully automated testing pipelines.

This platform supports both manual and automated testing within the same structure, letting teams scale easily without changing their workflows. With its focus on speed, transparency, and AI-powered optimization, Testomat.io gives engineering and QA teams the tools they need to deliver high-quality software—faster

2. TestRail

TestRail is a main place you can use for test case management. It is made for QA teams and helps with both manual testing and test automation. The tool lets you manage testing workflows in one spot, so you get more done.

TestRail gives you one place to keep your test cases. This helps you plan your tests in a good way. You can handle your test runs all in this space. You can link your test cases with what is needed and find defects faster. This makes it easy to follow the work from beginning to end. TestRail offers test automation tools like Selenium and Cypress that keep your testing workflows simple.

This tool is very flexible. You can change the fields and templates to fit your needs. It keeps your data safe because it meets secure standards like SOC 2 Type 2. The qa teams will find the reports clear and easy to read. You can see and know all about the work you do in testing. The tool works well with things like Jira. This will help you move easily through even the toughest projects.

3. Xray

Xray is a Jira tool that helps with test planning. It keeps test cases the same for all. With it, QA teams can work together in Jira in a better way. The tool lets you handle needs and test execution the right way.

There are some key features included. You get exploratory testing as one of them. With agile boards, you can see the progress in real-time. The shift-left setup lets developers and testers work together from the start. Xray also works with test automation frameworks like Selenium and JUnit. This means you can do the same things faster because of test automation.

Charts that show test results make it easy to see your coverage. Managers can know where things are, even for each small part of the work. If you want to move fast and keep things good for your team, Xray is a good match. Both the reports and the way you can use several interfaces fit what teams need when they do agile testing.

4. Zephyr

Zephyr is good for test automation because it is flexible. It helps QA teams if they have hard testing workflows. The tool works for both manual testing and test automation. You get speed and power with it. There is no need to worry about losing either one when you use this tool.

The test case repository helps your group keep track of all assets. It cuts out extra work so things run smoothly. This system works with well-known automation tools like Jira. People can manage their projects together more easily by using it. With the API interface, you get the test data from several channels. You can also connect to other tools with it in a simple way.

With strong analytics features, you can look at test results and spot problems fast. This means you get to fix what slows you down. So, your work gets better with each test you do. If you are sharing work or going over data to fix issues, Zephyr is good for teams of all sizes. It helps your team have smooth workflows and safe connections. This tool supports real success in test automation for qa teams.

5. PractiTest

For qa engineers who work with reporting and keeping track of tests, PractiTest is a good option. The tool gives you the flexibility you need with its features. You get hierarchical filter trees and dashboards. This helps make test case management easy.

You can track requirements and use testing workflows. This helps you stay on top of traceability. You are able to assign tasks and get feedback from people right away. The platform has smart reporting. You can use custom fields and show test info that is simple to read. These tools help teams make their test goals clear.

PractiTest can be used with bug trackers like Jira. This helps the team fix problems faster and easier. You can link your test modules to your main project goals. This makes sure your software stays on track with what you plan to do. PractiTest is made for teams who need to work together. It is good for teams who handle complex QA tasks.

6. Testmo

Testmo is a new test management platform that is made for QA teams. It is here to give you easy and clear answers when you are working with test automation, exploratory sessions, and real-time numbers.

QA teams get a lot out of this tool. They can use clear dashboards that help them watch test runs better. This lets people take out extra work. It keeps testing efforts on track with what the team wants. Testmo also lets qa teams link up with CD pipelines. This way, continuous integration works well and teams get updates that help them.

Testmo gives you features for exploratory testing that are simple to change. You can track where a session goes, but it does not get in the way of how your team likes to work. With powerful reports, your team can see progress, spot trends, and figure out better ways to do well as time goes on. When you use the test coverage tools that come with this platform, it is easier to know what still needs to be tested. This helps your team make smart choices for good QA.

7. QMetry

QMetry helps qa teams do better work in software testing. It uses advanced analytics and machine learning. This gives AI insights that can improve every part of the testing process. The tool helps people in software testing make their testing efforts easier and more clear. You get comprehensive test management because machine learning looks at test results and checks coverage.

When you use predictive analytics for test execution, you can find critical issues before they turn into big problems. This helps you make software development better and keeps you from big setbacks. The user interface is simple and easy to use. You can move through testing workflows step by step without trouble. Small teams and big groups both get to work better, fix defects faster, and keep their testing workflows smooth and well-managed.

8. Kualitee

Kualitee knows that it can be hard to keep test scripts up to date when things in the app change. That is why Kualitee has a self-healing test automation tool. This makes the testing process better for everyone. Kualitee uses machine learning. It looks for changes in the app and updates the test scripts by itself. You do not need to do as much by hand. This lets QA teams focus on other tasks.

This feature helps you keep test coverage strong, so your team can find more bugs. It can also make test execution faster because the tool can keep up with new or changing user interfaces. QA teams will find it easier to work with test case management. They will also get through the software development process faster. In the end, test automation with Kualitee lets teams do more and use their time well during software development.

9. TestMonitor

Real-time risk assessment is very important in software development today. TestMonitor makes a difference by using AI. It looks at test runs to find possible problems so qa teams can make quick and smart choices. With predictive analytics, the tool shows where likely defects can be and how they may affect things. This helps teams use their testing efforts in the right spots. The proactive way TestMonitor does things makes the testing process better. It gives better test case management and smooth workflows. This helps make higher-quality results and a smoother development process.

10. Tuskr

Tuskr uses smart systems to make the defect resolution process faster and easier. It finds issues in the code quickly, so you do not have to spend much time looking for problems. This test management tool uses AI and makes it simple for QA teams to find defects with little effort. With Tuskr, your team can use more time to make the testing process better, instead of going through a lot of manual reports.

The tool uses machine learning that works well with your testing workflows. You get full and complete test management for all your projects. The clear and simple interface of Tuskr helps you track defects. It makes sure that all critical issues are fixed fast. This leads to better software quality, and keeps people happy with what they use.

Key Features to Look for in AI Test Management Tools

When you want to put your money in the best test management solution, you need to know what key features matter for your project. The top things to look at are test automation tools that fix themselves, and that work well with other tools. You should also have real-time analytics that are clear and easy to read. Look for these features if you want a good test management solution.

The platform should help you with test case creation in an easy way. It must use smart tools like machine learning to help make things better. Try to pick a solution that can work with tough test cases but, at the same time, lets you track progress and see your test coverage without much trouble. When all these features are found in one place, you get a good way to meet different needs with less effort.

AI-Based Test Case Generation

Using artificial intelligence for test case generation changes how people do software testing. It lets qa teams work faster and with more accuracy. When machine learning looks at data from old tests, qa teams can make new test cases that cover different situations. This increases test coverage and makes the whole testing process easier. Because AI handles much of the work, teams do not have to do as many steps by hand. The team can then spend more time on important tasks. This helps them get more work done and makes their results better. When you use AI in test case generation, you get smarter ways to test. It fits well with the needs people have in development today.

Integration with Automation Frameworks

Seamless integration with test automation makes the work of qa teams faster and easier. If you use AI tools for test management that fit well with your test automation frameworks, you can improve how you test things and keep high test coverage at the same time. This lets your team manage test execution and test management tasks better. You will also get quick feedback in cd pipelines, so the work moves fast. The development teams and qa engineers get to work together more closely, which helps things go smoothly. Focusing on these integration features makes your automation strong, and it supports fast test case generation. Because of this, continuous testing turns into an easy job that works well throughout the process.

Real-Time Analytics and Reporting

Bringing real-time analytics and reporting into the testing process gives qa teams instant access to useful data. With this, teams get the information they need right away. They can change test plans or manage test case management as needed. This helps improve test coverage and sorts out problems faster. The new and smart charts or images make it easy to read and share test results.

This makes the work in an agile project management team much smoother. When development teams use these reports, they find critical issues much quicker. Over time, this brings better software quality, and the testing workflows get easier so teams can match project needs without trouble.

Self-Healing Test Scripts

Self-healing test scripts bring new ways to do test automation. These scripts can change when the user interface in the application changes. They use machine learning to find out what is new or different in the user interface. Because of this, the test scripts do not need people to always update them. This helps test execution to keep going well without too many stops.

Because of this, QA teams can use their time in a better way. They get to work on the most critical issues. They do not have to spend so many hours on normal updates. This helps boost test coverage. It also gives a good answer for places that change a lot, making the testing process more simple and easy for everyone.

How to Choose the Right AI Test Management Tool for Your Team

Choosing the right AI test management tool starts with looking at what you need for your project. Think about your team size and your test management process. The tool you pick should fit your team’s needs. Make sure this test management tool can work with any test management, automation, and continuous integration tools that you already use. This will help all your tools work well together.

You need to think about how easy the tool is to use. Make sure your QA engineers will not feel it is hard to learn. It is good to look at the pricing models. This way, you are not spending more than you should. Check that the tool has all the features you want for test management. It should fit your budget. When you take time to choose the right tool, your testing efforts become smoother. This will help your team save time.

Assessing Project Requirements and Scale

When you pick an AI test management tool, you have to look at the needs of the project and its size. Each project will have its own goal. It can be manual testing, or sometimes, you may need regression testing for mobile applications.

QA teams need to check the way they work and how they plan things. The team should know about the number of test cases to use, the number of test runs to make, and the tough parts of their software testing. All of this helps you find a good test management tool.

The right test management tool needs to fit all your diverse testing needs. It should make sure you get good test coverage in all of your software testing work. This tool must also work well with changes in your development process.

For example, if you are using agile project management or doing continuous integration, and you add new mobile applications, the tool has to handle that. All these things make your test management ready to grow and change with your team. This will help your testing efforts as your work gets bigger or different.

Evaluating Integration Capabilities

Integration is very important when you choose AI test management tools. It lets different software testing processes work together without trouble. When the tools be able to work with known automation frameworks and cd pipelines, development teams and qa teams get to work together with no big issues. This makes their testing workflows smoother. With good integration, qa teams can make test execution better and get feedback quicker. It also helps qa teams do more software testing in less time.

Being able to connect your tool to the project management system and bug trackers you have now is important. It helps the user experience. This also lets you get more test coverage. When these things work together, you can have higher quality software.

Considering Usability and Learning Curve

Usability is very important when you pick a test management tool. This means a lot for QA teams and for any project members. These people can have different technical skills. If the tool has a user-friendly interface, it makes testing easier and better. It also helps you add the tool to your testing workflows with less trouble. A tool with a low learning curve lets team members get going fast.

They do not need a lot of training to use it. If people want to know more about the software, they can check the tutorials and read the documentation. At the end of the day, you want to find a balance between ease of use and what the tool can do. If you do this, you will streamline testing processes and help your team get the work done well.

Comparing Pricing Models and Support

Looking at pricing models and support options is a big part of picking the right test management tool for software testing. A lot of platforms have their own way to charge you. Some use a monthly subscription. Some tools will ask you to pay one time only, and some let you pay based on how much you use them. Every pricing model comes with its own rules about what features, support, and updates you will get. You need to think about what works with your team, your budget, and your test management plans.

Good and quick customer support is also very helpful. You may need support when you start the tool or if you run into problems later. This type of support can make the user experience better for all people in your company. When you take your time to check these things, the test management tool you pick for software testing will fit your team just right. It will also be able to grow with your development process.

Conclusion

Choosing the right AI test management tool is important for QA teams. A good platform makes test automation and test case management much easier. It also helps team members work well together.

When you look for a test management tool, see how easy it is to use. Check what integrations it offers, and learn about its pricing models. This will help you find a tool that fits your specific requirements. The right choice can make testing jobs simple. It can also help development teams deliver higher quality work for their software.

Always look at what your project needs so that you pick the tool which matches your requirements best.

The post Top AI Test Management Tools appeared first on testomat.io.

With continuous testing, they can make the software testing process automatic and get it done quickly within the DevOps process.

However, they face difficulties when dealing with complicated CI\CD pipelines, strict security requirements, and dynamic cloud infrastructure. Thanks to artificial intelligence, that process is getting less labor-intensive and time-consuming. AI helps create systems that detect problems and heal themselves while optimizing performance and delivering software products which remain reliable, functional, and secure throughout their software development lifecycle.

What is Continuous Testing with AI?

Continuous testing is the practice of testing software continuously throughout the development cycle, typically as part of a continuous integration (CI) or continuous delivery and continuous deployment (CD) pipeline. By integrating testing into every phase of development, teams can catch defects early, improve product quality, and speed up delivery cycles.

When talking about Artificial Intelligence in the context of Continuous Testing, we mean embedding intelligent algorithms which can learn, adapt, and optimize the test cycles. By integrating AI algorithms in continuous testing, artificial intelligence technology helps minimize human involvement in executing tests, improve accuracy, optimize QA activities, and even start a self-healing process.

Generally, ML, NLP, robotics, computer vision, and other technologies come under the umbrella of AI in DevOps. Let’s reveal how they can power the continuous testing process:

• Machine Learning (ML). ML-based algorithms are useful in analyzing historical test data. They applied to identify patterns, make test case selection more effective, predict software defects, and learn from past processes of the test executions.
• Natural Language Processing (NLP). NLP can be used to turn test cases, which have been written in everyday language, into executable scripts and to avoid the need for complex scripting.
• Robotic Process Automation (RPA). With RPA, you can model human actions across various systems and environments to confirm that all the pieces of the app work together correctly.
• Computer Vision. Thanks to AI computer vision, UI elements can be recognized based on their visual characteristics rather than fixed positions, making tests more robust against layout changes and increasing the correctness of UI checks.
• Deep Learning. With deep learning, you can tackle highly specific challenges – identifying sophisticated vulnerabilities in your code repositories, scaling real-time anomaly detection in dynamic systems, and using automated root cause analysis to pinpoint the root cause of complex incidents.
• Predictive Analytics. Thanks to predictive analytics, you can learn information about future events and minimize risks in terms of selecting optimal deployment windows, adjusting resources when scaling.
• Chatbots and Virtual Assistants. These AI-based tools are useful when there is a need to automate interactions with the development team and provide real-time assistance during the development cycle.

We would like to mention that all these types of artificial intelligence require you to learn how to use them for your team’s needs. It’s worth remembering that they are just tools, which will only work if handled the right way in continuous integration automated testing.

Ways To Use AI For Effective Continuous Testing in DevOps

Here are some key ways artificial intelligence is changing continuous testing in DevOps:

Test Case Generation

Teams can apply AI-based algorithms when they need to save time and minimize their effort in creating and maintaining test cases. In this case, artificial intelligence is used to automatically generate test cases in accordance with requirements, user stories, past defect patterns, and code analysis.

Test Execution Optimization

When teams need to optimize the execution flow of tests based on real-time data and new changes in the software application, they can use AI algorithms. They are effective at assessing new code modifications and previous test outcomes to guarantee that the most critical tests are executed first. In addition to that, artificial intelligence can be used to provide parallel execution of tests across multiple environments. In the long run, it enhances test coverage and reduces execution time with faster feedback cycles.

AI-based Defect Prediction

When teams need to predict potential defects and resolve them before they escalate, artificial intelligence algorithms help them do it by analyzing test results and historical data, and identifying correlations between code changes and failures. In addition to that, AI can be applied to monitor application behavior and identify anomalies or unusual patterns in test executions. It can even detect slight visual changes in the user interface that might negatively impact user experience.

Self-Healing Test Suites

Teams have been forced to deal with broken test cases in terms of changes in UI elements, APIs, or system behaviors. Thanks to AI, the process of adapting to changes in the application under test is being done automatically while providing continuous and stable test execution in DevOps environments.

Regression Testing

With AI, the process of identifying and managing relevant regression tests, following unit tests, based on changes and risk assessments, which have been given to each selection, is highly effective and guarantees more comprehensive regression testing. In addition to that, artificial intelligence also investigates areas that need more test cycles, which makes sure they are managed and handled in the right way.

Proactive Continuous Security Testing

Artificial intelligence usage in DevOps can be applied as proactive security measures in order to identify security vulnerabilities through code modifications and code dependencies analysis. It detects security risks and discovers abnormal API call patterns in microservices, which allows teams to move security testing into the initial stages of development and minimize production threats. Also, artificial intelligence helps get real-time visibility and keep an organization’s digital presence under control through continuous attack surface testing.

Why Teams Need AI for Continuous Testing?

Now let’s talk about the main reasons your teams need an AI solution to streamline the testing process.

• Teams can optimize test execution and deal with the highest-priority areas first to prevent things that could really go wrong.
• Teams can win from AI’s capability to predict potential problems, even unusual situations that human quality assurance (QA) might miss.
• With AI, there is no need for the teams to perform time-consuming testing tasks, such as regression testing, exploratory testing, integration testing, performance testing, UI validation, and data entry.
• Based on deep artificial intelligence analysis, teams are in the know the original reasons for bugs, which means they need less time spent debugging and fixing.
• Thanks to artificial intelligence in test data management, teams can deal with faster data provisioning, automated generation, retrieval, and preparation of diverse types of tests instead of doing it manually.
• With artificial intelligence usage, teams can simulate complex and multi-stage attack scenarios and react by integrating continuous penetration testing directly into the CI\CD pipeline.

Key Benefits of AI in CI\CD

These were times when teams handled a lot of tasks manually. With modern technology, however, you can successfully use AI in CI\CD and reap the benefits:

• Pipeline Optimization. With AI, you can understand historical data of test executions and performance patterns. Knowing this information, artificial intelligence can change pipeline settings automatically, which helps find problems and anomalies, suggest fixes, and change resource usage in a jiffy. In the long run, it will lead to quicker build times and more stable deployments.
• Better Monitoring Capabilities. When using AI tools for CI/CD optimization, you can get real-time information about the QA process, alerts, logs, and error detection. Thanks to the ability to consider past logs and errors, artificial intelligence can quickly find the cause of pipeline issues and respond without manual work.
• Efficient resource usage. When applying AI, there is no need to plan resources manually. It automatically scales resources up or down based on what is required.
• Automated Tasks and Improved Security. When it comes to regular pipeline tasks like building, validation, and deploying code, artificial intelligence can automate them and reduce manual testing effort. In addition to that, there is an option of blocking or flagging risky code before it goes into the production environment.
• Code Quality Checks. AI-powered solutions for CI/CD look at code for bugs, style problems, and performance issues. It gives quick feedback to developers, helping them fix mistakes early and keeping the code clean and effective. If there are any issues, they are listed in the logs for manual review.

Challenges in Implementing AI in DevOps Testing

While we can see more benefits of artificial intelligence in DevOps testing, there are also challenges to its adoption. These include:

• You should take into account data quality and its availability. In order to work well, AI requires large volumes of high-quality data, which helps eliminate inaccurate predictions and inefficient QA processes. Furthermore, artificial intelligence should be continuously trained and fine-tuned to improve its accuracy, which requires expertise and resources.
• You may face integration complexity when trying to incorporate AI continuous testing tools with existing DevOps processes. To do this, you need access to quality data from various sources. However, data can be scattered across different departments or systems, making access and integration cumbersome.
• Many existing CI\CD pipelines and test frameworks lack built-in capabilities of artificial intelligence and necessitate additional setup.
• When you decide to integrate AI, you need to remember that it requires investment in infrastructure and whole team training. With increasing AI complexity, you need more computational power to run it.

Best Practices for Implementing Continuous Testing

Before implementing AI in continuous testing, you need to plan strategically and utilize the right continuous testing tools in DevOps. Here are some best practices for artificial intelligence in continuous testing:

• You need to remember that it is not effective to use AI to fix everything and everyone’s issues. It would be a good idea to define a specific testing challenge, which AI can solve. For example, improving test coverage in complex areas or identifying flaky tests.
• To work correctly, AI relies on data. You need to make sure the data is clean and representative to avoid unfair or discriminatory test results. Also, you should choose AI tools and techniques that align with your specific needs and infrastructure.
• You need to decide what you aim to achieve with AI and start doing it step-by-step. You can try the pilot projects without implementing a radical change, and apply AI where it can provide the most immediate value and help you attain established business goals.
• You should combine AI with existing QA methods. There is no need to replace human testers and traditional automation. The task is to automate time-consuming and repetitive tasks which require analyzing large datasets.
• You need to educate your team about how to use AI tools. Also, it is important to maintain documentation where all the information about the AI implementation process, training data, and integration processes is stored.
• You need to train AI models with fresh data to adapt them to your needs when scaling.

Meet more:

• Top 10 DevOps Testing Tools to Improve Your Testing Processes
• Azure DevOps automated Test Management and Testing Automation integration

Let’s sum up

Is Your Infrastructure Ready For AI DevOps Continuous Testing Services?

AI continuous testing is becoming a crucial part of modern CI/CD workflows. When you use AI for continuous integration and testing, this integration promises automation and intelligent decision-making, improves software quality and reliability.

If utilized correctly, AI CI\CD workflow will lead to more efficient, accurate, and reliable software lifecycles. However, you need to remember that AI workloads demand specialized compute resources, flexibility,  and technically prepared team members.

The key lies in a well-prepared continuous testing strategy, continuous learning, and balancing AI-driven test automation frameworks with human oversight and the principles of Continuous Integration (CI) and Continuous Delivery (CD) in mind.

Contact us if you aim to adopt this approach and benefit from faster release processes, improved software quality, and reduced risk of defects.

The post Continuous Testing: AI support in Software Testing appeared first on testomat.io.

With that in mind, development teams should not only create well-functioning AI-powered products but also carefully test them so that they adhere to ethical standards while delivering the best results and a great user experience possible. However, testing AI applications is a cumbersome process that requires a comprehensive understanding of the AI-based app’s architecture and built AI model into its functionality.

Testing AI Applications: What Are The Reasons?

Nowadays, more and more organizations have started the integration of AI and advanced features into their products. They encountered that AI-based software is not as predictable as traditional software. When teams build traditional software, which is designed to follow specific and rule-based logic, they do get a fixed and predicted result. But the situation differs if they work on artificial intelligence software, because it is more complex, dynamic, and unpredictable. Even when they test AI applications, it is mandatory for each team member to understand how the artificial intelligence model behaves, how it processes data and to check its ethical and accurate functionality. To sum up,

QA engineers need to:

Guarantee that the artificial intelligence system is able to complete tasks in the right way.
Check how well an artificial intelligence application performs under different types of conditions, how fast it provides responses, etc.
Validate how successfully an artificial intelligence app functions when teams compare AI-generated results against expected outcomes to identify errors.
Examine whether the AI-based system is biased from the start and in terms of it makes unfair decisions.

Principles of AI For Software Testing

• Transparency. When you develop an AI project, you need to understand why AI produces specific results, how an AI-based model makes decisions, and what data have been used for those purposes. It will help build trust and adhere to ethical standards.
• Human-In-the-Loop (HIL).  By incorporating feedback from human AI experts, it enables them to refine the AI’s algorithms. Once content is flagged by the AI, human moderators review what has been flagged to confirm whether it is correct or not. It improves AI’s ability to handle complex or uncertain situations and make more accurate decisions.
• Fairness and Unbias. Ethical implications like bias and fairness should be taken into account during the entire testing process. When an AI-based model delivers unfair results systematically, you should update your data, provide regular audits, review AI-driven algorithms, AI decisions, etc. to guarantee that the use of the AI system aligns with ethical standards.
• Accuracy and Reliability. Accuracy refers to an AI system’s ability to produce correct results while reliability means delivering predictable outcomes across various scenarios.
• Scalability and Performance. Artificial intelligence models should keep performance efficient when it comes to processing larger sets of data and more complicated tasks in terms of scaling. So, assessing the model’s ability to process increasing workloads without loss of speed or accuracy is essential in this case.

Types of AI-based Apps and Their Testing Needs

Quality assurance is important for all AI-based models and systems, such as:

Let’s break them down in detail next, keep reading

#1: Machine learning

Algorithms that can learn from data – by training on a large dataset, they identify patterns and make predictions instead of being specifically designed to perform certain tasks. These ML software require testing in order to:

1. Concentrate on the model’s ability to predict outcomes without mistakes based on labeled data.
2. Assess the model’s capability of finding hidden patterns or groupings in unlabeled data.
3. Validate how well the model learns a strategy to achieve maximal reward through repeated attempts.

#2: Deep learning

A kind of artificial intelligence model that is typically trained on large datasets of labeled data. The algorithms learn to associate features in the data with the correct labels and need testing in order to:

1. Make sure that the model fulfills its purpose on previously unseen data.
2. Detect whether the model has learned random data instead of underlying patterns.
3. Assess resource utilization during training and inference.

#3: Natural language processing

NLP models, which process information based on patterns between the components of speech (letters, words, sentences, etc.), are developed to understand natural language as humans do. They are tested in order to:

1. Identify whether they produce accurate results after they process human language.
2. Define whether they are able to keep context in tasks such as translation or summarization.
3. Identify whether the models are able to reveal human feelings, sentiments, and emotions, which are hidden behind a text or interaction.

#4: Computer vision

Algorithms, which are powered by computer vision technology, allow devices to identify and classify objects and people in images and videos. They are tested to:

1. Check if the algorithms correctly identify and classify images/videos, which have been processed.
2. Validate if the algorithms are able to accurately locate and identify multiple objects within an image/video.
3. Evaluate if the algorithms achieve consistent results even when they process under challenging conditions like occlusions, motion blur, poor lighting, and across different backgrounds.

#5: Generative AI Models

These models are trained on large datasets to uncover underlying patterns and learn how data is structured to generate new content – text, image, audio, video, and code. Testing generative AI applications is crucial to:

1. Assess generated content based on criteria such as fluency, creativity, relevance, etc.
2. To generate unique, logically correct, and diverse outputs.
3. Not to generate harmful or biased content.

#6: Robotic process automation

This is the software that is powered with artificial intelligence technologies to derive information from vision sensors for further segmenting and understanding scenes as well as detecting and classifying objects. In this case, they should be tested for:

1. Verifying that the robot completes intended tasks, even in different environments and scenarios, successfully.
2. Measuring the robot’s efficiency, speed, productivity, and accuracy when they perform tasks as well as meet safety standards, relevant laws, and regulations.
3. Optimize the robot’s algorithms, fixing errors that lead to equipment or environmental damage.

Key Factors To Consider While Testing AI Applications

When testing AI-based solutions, it’s important to take into account the following factors:

• Input/Output Data Balance. You should remember that not only input data but also intended outputs are essential when you test an artificial intelligence app. The task is to help the model handle real-world scenarios and deliver error-free outputs by using small data volumes and making changes in accordance with produced outputs, and only then to expand datasets.
• Training Data. You should use training data to learn an AI model from historical data to formulate the decision-making capabilities. Do not forget to review outcomes and adjust the model to improve accuracy.
• Testing Data. These data sets are logically designed to test all possible combinations and determine how well the trained models produce a desired or intended result. With the growing number of iterations and data, the model should be refined.
• System Validation Test Suites. With algorithms and test data sets, you create system validation test suites to test models to make sure that they are free of problems.
• Reporting Test Findings. QA team members must specify confidence criteria between specified ranges when validating algorithms. Just because range-based accuracy or confidence scores work for artificial intelligence algorithms better instead of test results presented in statistics.

Testing Strategies To Use When Testing AI Products

When developing and testing artificial intelligence products, it is essential to avoid unnecessary problems. Here, a well-planned and implemented testing strategy can be helpful. It looks like a plan that describes the process of testing and includes information about objectives, the scope of testing including information about test data generation, test creation, test execution, test results, test maintenance, edge cases, testing methodologies and approaches, software test automation tools, and so on. Below, we are going to overview approaches that are critical for testing AI applications:

There are three approaches that can be used during the AI lifecycle:

1. Data-centric testing. This approach is applied to test the quality of the raw data used to train and assess AI models by checking the completeness, accuracy, consistency, and validity of the data, detecting biases and drift.
2. Model-centric testing. This approach is utilized when the task is to test the overall quality of the AI-based product, and where performance testing, metamorphic testing, robustness testing, and explainability testing are used.
3. Deployment-centric testing. This approach is applied to test the quality of the delivery of AI-based products through scalability testing, latency testing, security testing, and A/B testing.

Also, it is important to mention that you need to carry out automation testing to test AI-based app functionality that is powered by an AI model and use the following set of test cases:

• With unit tests, teams can check the functionality of the app and some components of the AI-driven model.
• By using integration tests, teams can check how data flows between the app and the model work together.
• When applying performance tests, teams can evaluate the app’s and AI model’s speed, and efficiency, as well as reveal information about how resources are utilized.
• With e2e tests, teams can create simulations of user workflows and evaluate the overall system performance.

How To Test AI Applications: Key Steps

When we talk about carrying out effective testing for AI-based solutions, it is important to mention that the functionality of these products is built around an artificial intelligence model that processes data and generates insights. This means that you should combine traditional test automation approaches with AI-specific techniques. The following steps are helpful for testing AI applications and their model, which is integrated into the application’s architecture, to make sure it provides the best results possible for end users.

Step 1: Prepare Data For Testing AI Applications

Before starting to test, it is important to collect and assess the quality of the data, which will be used for training/testing purposes. You need to remove all inaccuracies and inconsistencies from the data and organize it into a format which AI algorithms can understand and interpret correctly. Furthermore, you need to remember that if the data contains errors, is incomplete, or has biases, the AI-based system will deliver the wrong results, and it will not be able to complete the necessary tasks or imitate human behavior.

So, you need to check for missing or incorrect values in the data, make sure that the data covers all possible scenarios (e.g., different age groups, genders, regions, etc.), and divide the data for different processes – training, validation, and testing which help you avoid overfitting.

Step 2: Incorporate Training

Once you have your data, you can use it to train the AI model based on carefully selected data and allow it to learn and start making predictions or decisions. If artificial intelligence models make mistakes at this early stage of learning, you should correct them to improve the model’s accuracy.

Step 3: Focus on AI Validation

At this step, you need to validate the accuracy of the AI model. To do so, you need to have a separate dataset for validation, which is broader and more complex than the training one. When validating, you can uncover any security issues or gaps in the model’s ability, which are difficult to do with training data. In addition to accuracy, you can check how often the model makes correct predictions as well as accurately classifies.

Step 4: Perform AI Testing

Only after the data has been validated, you can start the testing process in order to evaluate the artificial intelligence model against different scenarios and use cases. Development and QA teams, which assess the model’s performance, simulating real-world conditions that help them detect potential challenges/limitations and solve them so that the artificial intelligence model will provide trustworthy and unbiased results. However, in case the model does not offer the desired accuracy, it must go through the training stage again.

Step 5: Deploy

Once the model has been tested, it is time for deployment into the intended infrastructure – on-premise or cloud environment. In the live environment, the model will provide recommendations and make predictions, which may happen. When deploying the model, teams should take into account the scalability, reliability, and security of the infrastructure in advance, because the chosen environment for deployment should be able to handle the expected workload and user interactions as well as keep the data protected.

Step 6: Analyze and Improve

At this stage, it is critical to analyze various aspects of the behavior of the artificial intelligence model – errors in accuracy, instability of the data, and wrong decisions or predictions: all these impact the results. By closely monitoring the performance of the artificial intelligence model, you can use a combination of statistical analysis, automated monitoring systems, and periodic reviews/feedback to detect potential issues and take appropriate steps to make changes in the algorithms. When applying continuous testing, feedback, and defect analysis, teams can launch powerful AI models, which not only meet the specific needs of the project or application but also produce results faster and with more precision.

Challenges in Testing AI Applications

When it comes to testing AI applications, it may present several challenges that can impact their effectiveness and reliability. Key issues include:

Unrealistic Expectations

When testing AI/ML-driven projects, there is a possibility of facing requirements which may be unclear. Teams which create traditional software projects start with clear and well-defined requirements. Otherwise, requirements for AI/ML products might be unrealistic and processes that need AI impact are not identified, leading to failures in terms of unclear goals of what AI-based app aims to achieve.

Data Imbalance and Bias

When teams work on AI-based projects, they may deal with biased results and inaccurate predictions, because AI models may be trained on imbalanced datasets. That’s why it is crucial to uncover imbalanced datasets and to stop dealing with them by carefully collecting and preprocessing data. To solve this problem, teams can apply under-sampling, over-sampling, and synthetic data generation techniques to improve the performance of the model.

Interpretability Issues

Teams, which test AI software,  face the complexities of AI algorithms and model decision-making processes. They find it difficult to discover how AI makes predictions and decisions as well as its ability to perceive or recognize complex patterns. If the process isn’t transparent, the situation really poses challenges to the ability of the model to adhere to regulatory standards. Thus, teams should incorporate explainable artificial intelligence techniques (SHAP, LIME), which can help them enhance interpretability and provide insights into model behavior.

Absence of Established Testing Standards

Without universally accepted tools for AI model testing in the AI-QA ecosystem, teams have problems with inconsistencies when they evaluate and validate models. In addition to that, they don’t have established standards for data formats, workflow integration, etc. Incompatibility between tools for AI app testing and existing QA frameworks makes the AI software development process more complicated.

Lack of Resources

To test AI models, it requires major initial investments, which include computing power, training the team or hiring new specialists. When models scale and grow in complexity, they are trained and tested on larger datasets which means it demands significant computational resources. As a result, you need to make sure that teams are equipped with high performance computing infrastructure, which helps them better manage these demands and not worry about the models’ ability to scale more effectively.

AI Software Testing Tools and Frameworks

Testing AI applications requires a blend of manual effort and specialized AI automation testing approaches. Below, you can find some special AI and software testing tools:

• TensorFlow Extended (TFX). Designed by Google, this open-source platform allows teams to manage machine learning models in production environments as well as speed up ML workflow, from data collection and pre-processing to model training and deployment.  
• IBM Watson OpenScale. Being an open environment, it allows teams to manage the AI lifecycle and test AI/ML models with reduced test cycle times. It also offers options for monitoring AI/ML models on the cloud or anywhere else where they might be deployed to guarantee their accuracy and fairness.
• PyTorch. Built on Python, it is an open-source machine learning framework that includes libraries for testing AI-driven models and assessing their performance in different types of data, like images, text, and audio.
• DataRobot. It is an automated machine learning platform that supports various languages and allows teams to develop, deliver, and monitor AI models. The platform is also useful if ML models scale and develop.
• Apache MXNet. Having an easy-to-use interface, this open-source deep learning framework allows teams to define, train, and deploy deep neural networks across a diverse range of devices.   

Tips for Better Testing AI Products

• Teams should understand from the start the problems and goals that should be solved with the development of an AI-based project.
• Teams should be attentive during the process of data collection, data evaluation, and test case creation to make sure the data is clear and relevant.
• Teams should retrain and recheck AI-driven models once new data have been incorporated or updated or task requirements have been changed in order to keep the models relevant and effective.
• Teams should have enough data for testing AI applications to produce more accurate results.
• Teams should follow regulations like GDPR to meet strict data standards and avoid bias and misuse.

What About Testing AI Applications to Stand Out From The Crowd?

There’s no doubt that any business can reap the benefits of AI. But investing in AI-powered systems is not enough – you should thoroughly test them and improve test coverage.

Also, remember that testing AI software is an ongoing process, which evolves with technological advancements. With that in view, you need to regularly test AI-based products to carefully assess if they are transparent, unbiased, highly reliable, and accurate.

Only by implementing the best tips, using modern software testing AI tools and test automation platforms can you create AI-based products that address the challenges and requirements of today’s digital world.

Don’t hesitate to contact our specialists if you have any questions about testing AI applications.

The post Testing Strategy for AI-Based Applications appeared first on testomat.io.

In this article, we will reveal what AI automation testing is, its key components and common use cases, overview benefits and limitations, and share actionable tips to help you get started with AI-powered test automation. Let’s get started

What is AI in Test Automation?

When we talk about AI in testing automation, we mean a type of software testing where artificial intelligence (AI) is applied to improve and make the testing process more streamlined and simple. Originally, AI automation testing is faster when there is a need to retrieve data, run tests, optimize test coverage, and identify bugs and other anomalies.

Key Components of AI For Automation Testing

• Machine Learning (ML). ML-based algorithms are main in AI automation testing in terms of their ability to learn from historical data, identify patterns, and forecast potential defects or anomalies.
• Natural Language Processing (NLP). In the context of testing, NLP equips AI automation testing tools with the ability to understand and interpret human language. While testing teams can use plain language for writing test cases, the AI automation testing tool can then turn them into scripts for further execution.
• Data Analytics. With advanced data analytics incorporated into AI testing tools, teams can assess large volumes of test data and extract meaningful insights. Artificial intelligence can also be used for test results analysis to detect recurring issues or performance faults.
• Computer Vision. AI-driven image recognition helps detect visual anomalies in highly descriptive UIs and maintain consistent visual layouts.
• Robotic Process Automation (RPA). When RPA integrates with AI, it enables the automation of repetitive, rule-based tasks – data entry, report generation, and environment setup, which can be performed within the testing lifecycle to let testing teams concentrate on more strategic activities or processes.
• Self-Healing Scripts. With these scripts, AI-based tools can automatically update scripts when either the UI or code changes, minimizing the maintenance efforts of the software development team.

Use Cases of AI in Test Automation: How to use AI in automation testing

Artificial intelligence has had a major impact on automation testing that we can’t ignore. The uses of artificial intelligence in software testing for automation cover more use cases:

API Testing

With AI tool for automation testing, the process of API testing is more simple in terms of faster test cases generation, responses validation, and continuous monitoring of API performance. When used, it provides thorough API test coverage and helps detect issues before they impact production.

Visual Testing

AI-driven visual testing tools are used to detect UI inconsistencies across different environments. They can analyze screen elements, validate layout transitions, image misalignments, and incorrect colors to make sure that user interfaces are pixel-perfect based on the number of visual changes incorporated.

Performance Testing

When applying AI for performance testing, it enables analyzing performance data and predicting potential bottlenecks in the application that is being tested. Thanks to this approach to performance testing, developers can address performance issues in early development process.

Analytics for Test Automation Data

Tests generate vast amounts of data, which must be analyzed to derive meaning. The addition of AI to this process significantly improves its efficiency. AI-powered algorithms may discover and classify faults. More powerful AI systems can detect false negatives and genuine positives in test scenarios.

Predictive Analytics for Defect Detection

With predictive analytics, testers apply historical data from previous test results, code quality statistics, and defect patterns to create ML-based models. These models will help them uncover potential defects and predict future bugs by analyzing current test results in real time and identifying patterns and anomalies. Thus, they can optimize their testing strategies and accordingly allocate resources.

Generative AI automation testing

Generative AI generates information from diverse sources to create an array of test cases that cover a wide spectrum of scenarios. It provides a comprehensive testing process across a wide range of data inputs and helps in detecting potential bugs and anomalies.

AI-Assisted Bug Detection

AI can identify patterns that indicate potential bugs or issues even before traditional tests have been run. This predictive capability can help testers focus on areas that are more prone to defects.

Codeless Testing

Testing without code allows the testing teams to create automation test scripts without using programming languages. With visual interfaces, drag-and-drop functionalities, and sometimes natural language processing, they are able not only to design but also keep control of test cases through more intuitive and user-friendly ways.

Natural Language Processing for Test Design

With NLP, tools can extract requirements from user stories, use cases and functional specifications and automatically generate test cases in a structured format. They can also update test cases as requirements evolve to reduce manual testing effort and ensure better test coverage.

Self-Healing Test Automation

AI-driven algorithms in self-healing test automation identify, analyze, and dynamically update test scripts whenever application changes happen in the UI. It saves time and effort for QAs to maintain test scripts and continue execution of test cases, even when changes are made to the app under test.

Simulation & Virtual Testing Environments

AI-driven test automation can be used to create virtual environments where software can be tested under different conditions and scenarios. By simulating real-world situations, teams can test the software’s robustness and resilience – either network disruptions or hardware failures. Here we can include cross-browser testing.

Mobile AI Driven Automation Testing

AI-based tools can analyze UI and user interactions in mobile applications and check layout inconsistencies and performance issues to speed up mobile testing. Simulate testing on various devices.

Security Testing

AI test automation tool is built to scan code for security loopholes and find weak points in both APIs and web applications. It helps detect vulnerabilities and prevent potential cyberattacks and data breaches before deployment.

Benefits of AI software testing automation

Now, traditional testing methods struggle to keep up because organizations strive to deliver software solutions faster. With AI-driven test automation, they significantly streamline the testing process. Let’s discover more benefits below:

• Teams eliminate the need for manual data creation by automatically generating test cases and maintaining scripts while enhancing the efficiency of the process.
• Teams analyze historical and current data, predict areas which are likely to fail, and proactively address them before potential issues arise.
• Teams can focus on high-risk areas of the application and, through intelligent test execution, run tests based on various factors, such as code changes, historical results, and user behavior analytics.
• Teams integrate AI into CI\CD pipelines to carry out continuous testing.
• Teams identify edge cases and provide continuous testing coverage.
• Teams detect issues faster, leading to long-term cost savings.
• Teams quickly resolve UI issues to improve the overall user interface, delivering a more aesthetic user experience.

Limitations of AI in automation testing

Despite its benefits, AI automation testing has some drawbacks to consider:

• When it comes to implementing AI-powered testing tools, it demands an initial investment of money, as commonly AI-powered testing platforms require a subscription.
• AI systems require training and expertise from team members in order to manage AI test automation more effectively.
• AI can’t perform exploratory and usability testing, which require the intuition of humans.
• While AI can reduce test maintenance, it still requires oversight and periodic updates.
• There is a need to have enough quality data for AI’s effectiveness.
• In terms of “low-code” platforms, test creation is getting more intuitive and accessible to non-engineers.

AI and Automation Testing: Tips To Follow

When you think of implementing AI in test automation to streamline processes and improve testing efficiency, it is crucial to follow some key strategies to get the best results from AI integration.

Here are some tips to help you implement AI in your test automation process:

• From the very start of using AI in your testing process, you need to define what you want to achieve with it – improve the test coverage, reduce the test execution time, speed up test case generation, etc.
• You need to train your QA teams so that they can use AI tools effectively.
• You need to understand how the AI tools work and how to use them. So, start by automating a few key areas to try it and only then scale up.
• You need a lot of relevant data to train AI to produce the best results. If you use a small or biased dataset, you face overfitting and get unreliable results.
• You need to balance the testing capabilities of AI tools with human problem-solving skills to get the most out of the AI test automation process.

Check out some of our other posts:

• AI Testing Tools: An Effective Way to Optimize Your QA Processes
• Playwright AI Revolution in Test Automation
• AI Mobile Testing – How To Use It Effectively
• Role AI testing, in particular, ChatGPT in Software testing

CodeceptJS AI Self-Healing Capabilities in Your Testing

Codeceptjs testing framework is one of our team’s developments, embraced by teams worldwide. It supports the AI Test Self-healing functionality for Automated tests, which boosts UI test reliability by smartly fixing broken selectors. When UI updates alter classes, IDs, or DOM structures, static locators often fail. Codeceptjs analyzes past selectors and pattern matching to locate elements, keeping tests on track. Look of how it works:

The key benefits of Codeceptjs AI Healing are the slashing of maintenance costs and flaky test failures in dynamic web apps.

Powered AI Automation Test Management

Test Management System plays a crucial role in the testing process, especially when it is fueled by AI-powered automation testing. This combination is a key in modern testing ecosystems. Not only provides sync manual and automated tests in a single test platform for efficient organization, execution, and analysis. It drives efficiency, quality at speed, and smarter QA decisions in automation.

You can see the stack trace and exception to know where the error is located.

AI-Driven Reporting & Analytics apply AI to historical test trends, a TMS can surface intelligent insights, such as predicting flaky tests, identifying performance bottlenecks, or highlighting coverage gaps. This empowers teams to make faster, data-backed QA decisions.

If you press the button Explain Failure, you can see the recommendation on how to fix the framework broken by the error in the code.

Bottom Line: Future of AI in Test Automation

AI technology changes test automation thanks to intelligent test case generation and test cases management, bug/issue detection and report generation. In many ways, AI speeds up software testing and automates repetitive tasks, but there is no one-size-fits-all solution in testing. QA testers are still irreplaceable for their cognitive skills, creativity, and problem-solving abilities. In many ways, AI can not identify unexpected user behavior or minor inconsistencies in interfaces.

Even with more sophisticated intelligent ai based automation testing solutions, the future of automation testing succeeds through a collaborative approach where testers can use their emotional intelligence while AI assists them. However, if AI-powered tools could create self-healing tests which automatically adjust to UI changes, it would be really innovative. We are convinced that using the strengths of both AI and human experts leads to achieving the highest quality software possible. If you are interested in AI-based automation testing or have any questions, feel free to reach out to us here.

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