— Are you surprised

— The reason? Flaky tests are!

This article explores how to identify, eliminate, and prevent dangerous flaky tests.

Unstable tests can become a serious obstacle that complicates the development process. They create uncertainty and require significant time and resources to resolve when they are detected.

→ What do flaky tests entail?
→ What triggers them?
→ How can they be fixed or prevented? — it is the most important for us.

You will find answers to all these questions below

What Is a Flaky Test?

A flaky test means an automated test that produces inconsistent results. We can talk about a spontaneous test failure and a successful pass during the next test execution. This behavior is not related to any code changes. Naturally, this type of software testing does not contribute to overall Quality Assurance. In other words, it prevents teams from effectively reaching their objectives.

Key characteristics of flaky tests include:

• Inconsistency of results. Flaky tests produce unreliable outcomes, as their results fluctuate regardless of code changes.
• Unreliable test status. Assessing a product with flaky tests is inherently unreliable, as the results cannot be trusted. The Pass and Fail statuses fluctuate randomly with each retry, making them unpredictable and difficult to interpret.
• Dependence on external factors. These tests are extremely vulnerable to external dependencies, including environment variables, system configurations, third-party libraries, databases, external APIs, and more.

We have now defined flaky tests and outlined their key characteristics. However, the common causes of their occurrence have not been revealed yet.  Let’s delve into this topic

What Causes Test Flakiness?

Understanding the root causes of flaky tests will allow you to develop an effective strategy for their prevention. It will also help you mitigate the consequences if they do occur.

What can serve as precondition for future flaky tests?

→ Parallel test execution. Running tests concurrently can enhance the efficiency of QA processes. However, in some cases, this approach may backfire, leading to test instability. This happens when multiple tests compete for the same resources. In other words, race conditions are present.

→ Unstable test environment. A project may face unreliable infrastructure or fluctuating system states. Insufficient control over the environment or lack of isolation can also contribute to instability in testing.

→ Non-determinism. This refers to generating different results from the same set of input data. In testing, this can occur when tests depend on uncontrollable variables, such as system time or random numbers.

→ Errors in test case writing. These can result from misunderstandings within the team, incorrect assumptions, or other factors. As a result, the test logic may be compromised, leading to unreliable results, such as false positives or false negatives.

→ Partial verification of function behavior. When creating test cases, it is important to write as many assertions as possible. They should cover all aspects of the function’s behavior, touching on edge cases and accounting for all potential side effects. If this is not done (if the assertions are insufficient), the tests risk becoming unstable.

→ Influence of external factors. Some dependencies can negatively impact test stability. To illustrate, here are a few examples:

• Tests that rely on external services or API can become unstable.
• Problems with data consistency or synchronization can arise if testing is related to a database or external storage.
• System issues. Issues like high server load or memory overload can undermine stability.
• Device dependency. Instability may also arise from problems related to hardware availability.

Most of these preconditions for test instability can be eliminated, thus preventing future issues. However, if this is not achieved, it is important to be able to recognize flaky tests in time.

Learn more about the causes of flaky tests in this video: What Are Flaky Tests And Where Do They Come From? | LambdaTest

How to Identify Flaky Tests?

In this section, we will discuss how to determine that your test suite is not reliable enough due to the presence of flaky tests. It is crucial to do this, as ignoring the issue can reduce trust in the CI\CD pipeline overall and slow down development.

Here we are describing them in detail:

#1. Re-running Tests

Examine the test results when executed multiple times. If conflicting results arise during this process, it’s a clear indication of flaky test.

#2. Alternating Between Sequential and Parallel Test Execution

Test both sequential and parallel executions, then compare the outcomes. If a test fails only during parallel execution, this could point to race conditions or test order dependencies.

#3. Analyzing Test Logs

Reviewing the test execution history and error messages can reveal patterns in failing tests. For example, tests that produce different errors across runs may signal non-determinism or insufficient assertions.

#4. Testing in Different Test Environments

Run tests in various environments with different configurations or resources. If the results vary, it’s a sign that the tests may not be stable.

#5. Focusing On External Dependencies

Pay special attention to tests that depend on external factors. These may include API, databases, file systems, etc. These tests are more prone to unstable behavior. Potential failures may be triggered by issues with the external system.

#6. Using Specialized Tools

The CI\CD pipeline is an ideal place to spot flaky tests, as it tracks the success and failure history of individual tests over time. Many CI\CD tools also offer additional plugins designed to monitor instability.

Modern test management systems like Testomat.io can also assist in detecting and diagnosing flaky tests. We’ll dive into the platform’s capabilities for this later.

#7. Manual Checks 

If test flakiness is still not obvious, you can try to detect potential flaky test cases manually. To do this, check the test codebase, evaluate the likelihood of race conditions, and analyze the test logic. In other words, assess the presence of any instability causes we mentioned earlier.

These reliable strategies will help you identify flaky tests. Why is this crucial for project success? We break it down.

The Importance of Flaky Test Detection

Test instability is an issue that many teams face. The results of a recent study showed that 20% of developers detect them in their projects monthly, 24% weekly, and 15% daily. Interestingly, 23% of respondents view flaky tests as a very serious issue.

Here are several reasons behind this perspective, highlighting why it is crucial to identify and address instability promptly:

1. Slowing down the development process and increasing project costs. Unreliable test results prevent teams from progressing to the next development stage. They require manual checks, repeated executions, or extra steps to pinpoint and fix errors, consuming valuable time and resources.
2. Decreasing the effectiveness of test automation. Flaky test outcomes provide little useful information, leading to a loss of trust in the entire test suite. Over time, teams may begin to disregard test results, undermining the purpose of continuous integration systems.
3. Inconsistent feedback. Instability in tests results in inconsistent feedback on the quality of the application code. Developers fail to get an accurate picture of the situation, which delays problem identification and resolution.
4. Decreased team performance. Frequent failures can negatively impact team morale, leading to diminished productivity, communication, and motivation. This ultimately affects the quality of the final product.
5. Challenges in identifying true errors. Flaky tests in the test suite, may cause developers to mistakenly attribute all failures to these inconsistencies, overlooking real issues in the codebase. As a result, these problems remain undetected, accumulate, and create major challenges in diagnosing and resolving them.

Flaky tests disrupt the software development process in many ways. From increasing the duration of project work to worsening the overall atmosphere within the team. This is why it is important to identify and eliminate them as they arise.

How to Measure and Manage Flaky Tests?

The initial step in effectively managing flaky tests is to evaluate their frequency and the impact they have. This can be done through different methods:

• Analyzing test run history. Review the test execution history in your CI\CD pipeline or version control system. This will help identify the number of tests that periodically change their pass/fail status regardless of code modifications.
• Evaluating failure frequency. Track how often tests fail under varying conditions, such as in specific testing environments.
• Using test automation metrics. To gauge the extent of the issue, calculate the flakiness rate. This metric represents the percentage of test runs that produce unstable results. It is calculated using the following formula:

• Applying statistical methods. For example, you can use the Standard Deviation/Variance measurement method. If there is no instability in a specific test suite, the standard deviation will be zero.
• Using specialized tools. Some modern platforms enable teams to optimize their testing efforts by analyzing test result trends and helping to identify and manage flaky tests.

Test Management AI-Powered Solution for Flaky Tests Detection

Test Management System testomat.io is a powerful TMS that offers its users advanced capabilities for working with automated tests. One of these features is advanced test analytics, offered through a Test Analytics Dashboard with user-friendly widgets.

One of the key widgets in the system is Flaky Tests. It allows testers to easily track tests with inconsistent results and make decisions about fixing them.

Let’s take a closer look at the algorithms used to detect flaky tests in Testomat.io. On what basis can a test be added to this list?

To identify instability, the system calculates the average execution status of a specific test. The following parameters are used for the calculation:

• Minimum success threshold. The minimum acceptable percentage of a “pass” status, which can be considered an indicator of instability.
• Maximum success threshold. The maximum acceptable percentage of a “pass” status, which can be considered an indicator of instability.

Let’s consider how the system’s algorithms work with a practical example.

Set success thresholds:

• Minimum – 60%.
• Maximum – 80%.

Suppose a test was run 18 times. Out of these, 12 runs were successful. So, its success probability = 66%. We see that the obtained result falls within the specified range. Therefore, the test will be considered unstable.

Note: To calculate the passing score, the data from the last 100 test runs are considered.

After displaying the table with flaky tests, users can perform the following actions:

• Sort with one click. To do this, click on one of the required columns – Suite, Test, Statuses, or Executed at.
• Filter by execution date, priority, tags, labels, and test environment.
• Change the order of columns for easier data analysis.

So, we have learned how to detect flaky tests, assess their impact on development quality, and manage them with specialized tools. Let’s move on to methods for addressing the problem.

How to Maintain Flaky Tests?

Effective maintenance of flaky tests involves several stages. Together, they allow you to fix existing instability, understand its cause, and prevent it in the future.

Root Cause Analysis

Identify the cause of instability. The most common causes include resource unavailability, external dependencies, errors in test code, or race conditions.

Fixing Flaky Tests

After pinpointing the cause of instability, take corrective measures to eliminate it. These may involve:

• Ensuring test idempotency. Tests should be designed to run independently, without relying on previous executions to maintain consistency.
• Implementing synchronization mechanisms. This is necessary so that tests fail when race conditions or system delays occur.
• Simulating external dependencies. If the cause of instability is a test’s dependency on external services, it is advisable to use stubs. They will simulate the dependency.
• Stabilizing the test environment. It is crucial to ensure maximum stability and predictability of the environment. One option is to use containerization.
• Improving the quality of flaky test cases. Control test logic and cover as many system or function behavior scenarios as possible.

Isolation and Prioritization of Problematic Tests

This step involves categorizing flaky tests by severity. If a test frequently fails, the issue should be addressed as a priority.

The most unstable tests should be isolated or temporarily removed from the overall test suite. Alternatively, use a relevant tag to mark such test cases. This way, you can minimize the impact of flaky tests on overall test results.

Continuous Monitoring of Tests and Team Training

Even after eliminating instability, continue to monitor your test sets continuously. This will allow you to:

• ensure the effectiveness of the fixes made;
• prevent flaky tests from reappearing;
• maintain a feedback loop.

It is also important to provide ongoing training for testers throughout the project. This will help them write reliable, stable tests, including:

• correctly handling external dependencies;
• considering all possible function behavior scenarios;
• following test isolation methods;
• avoiding race conditions.

Effective maintenance of flaky tests includes identifying their causes, working on their elimination, and subsequently monitoring the quality of test cases. Combined with continuous improvement of test automation skills, your team will achieve good results in solving this problem.

Summary: Best Practices for Minimizing Flaky Tests

Flaky tests are a serious problem faced daily by many development teams. To bring a quality software product release closer, it is recommended to focus on reducing their number. How can this be done?

• Ensure test isolation. Tests should not depend on the state of previous tests. It is also important to test in isolated environments. Containers or virtual environments are suitable.
• Avoid tests that rely on time. Do not rely on waiting for a fixed amount of time. Instead, use timeouts or explicit waits.
• Simulate external dependencies. If a test depends on external services, use mocks and stubs. Instead of databases and API, you can use mocking libraries.
• Use reliable test data. It should be predictable. Avoid depending on dynamic data, as any changes to it can cause instability.
• Ensure reliable synchronization. Parallel test execution should be carefully managed. Use locking mechanisms like semaphores or queues to ensure tests run consistently and prevent race conditions.

Implementing these strategies will help minimize the chances of instability in software testing for your project. As a result, your team will save time and resources that would otherwise be spent on fixing it.

The post Overcome Flaky Tests: Straggle Flakiness in Your Test Framework appeared first on testomat.io.

In simpler terms, it is a concise description of all the test cases conducted, the objectives intended to be achieved, and the results obtained after the completion of the entire testing process, adhering to exit criteria.

Thanks to the test report in the test management system (TMS), collaboration between the team and stakeholders is improved, ensuring a shared understanding among all participants in the development process.

Unlike daily prompts software testing reports, which highlight only the current status and daily results, the test report with TMS contains summarized archived data history about all testing activities performed throughout the software development lifecycle.

Advantages of Test Report in Software Testing

A test report in software testing is a useful artifact and tool for both the QA team and all stakeholders for several reasons:

1. It provides information about the quality status and readiness of the software application to both the internal team and external stakeholders (such as developers, project managers, and product owners). Especially available reporting promotes transparency between teams, where everyone can see the current status of the project and also they together might perform root cause analysis of specific bugs.
2. A good test report includes metrics that help the QA team know the work’s effectiveness. Visualizing these metrics offers valuable insights for shaping future testing strategies and uncovering areas for future improvements.
3. Test reports are usually aligned with the test plan, so they are critical issues for monitoring the overall progress.
4. Test report verifies that the product complies with established requirements and standards.

The Test Cycle Closure Report provides a comprehensive summary of the entire testing phase. Include key metrics like test cases executed, passed, failed, and any unresolved defects to give stakeholders a clear overview of the testing outcomes.

Sergey Almyashev
COO, ZappleTech Inc.

Test reports in software testing, along with their thorough analysis, help to greatly enhance the development process by offering accurate and timely feedback.

Common Test Reporting Functions

Recording Test Results (Data Collection)

A substantial quantity of data is produced during the testing process, including coverage metrics, reports on defects found, testing results, and other data. The report’s structure and organization guarantee the relevance and dependability of these data. Details of the test environment, including hardware and software configurations, are included in the gathered material in addition to test execution results. This gives interested parties a better grasp of the testing circumstances and the variables that might have affected the results.

Data Analysis of the obtained result

After the relevant information is collected, a thorough analysis is performed to identify trends and connections. The main goal is to evaluate the performance of the software and identify potential problems. For example, the analysis may reveal that a specific functional area contains recurring bugs, indicating that the team needs to focus on improving that specific element.

Presentation of Results

In the final step, test results are presented in a clear and practical format to allow for quick decision making. Visualization tools such as charts, graphs, or dashboards are often used to provide a snapshot of the test status in a matter of seconds. Additionally, text annotations are included to provide context and explain the meaning of the presented data.

Types of Testing Report Protocols

There are different types of test reports, each containing important information and key metrics about the tests performed:

1. General Test Report. Provides a general overview of all test activities performed during a specific project phase or cycle. This report reflects metrics such as the number of test cases: successful and failed, as well as unresolved defects, helping participants in the testing project assess the overall state of testing. You understood right, it consists of different more detailed parts, which we consider at the bottom.
2. Test Execution Report (TER). This report provides detailed information about each test, such as the test case ID, description, status of each test case (pass/fail), and any additional notes from the tester. It helps track testing progress and highlights areas that may require urgent attention.
3. Bug Report/Error Report. Contains details of any bugs found, such as their identifier, description, severity, priority, reproduction instructions, and current status (e.g., open, fixed, closed). Bug report helps prioritize fixes and track progress in fixing bugs.
4. Traceability Matrix. An RTM table that shows how each project requirement maps to a test case, ensuring complete functional coverage and demonstrating the test coverage achieved.
5. Smoke Test Report. It verifies the critical App’s functionalities after the latest build deployment.
6. Performance Report. Evaluates the results of performance testing, including response time, throughput, utilization, resource allocation, and scalability, thereby assessing system performance under various loads and identifying potential performance issues.
7. Security Report. Describes the results of security testing, including discovered vulnerabilities and recommendations for improving security, especially for applications that handle sensitive information.
8. Regression Testing Report. Summarizes the results of regression tests, evaluating the impact of new features on existing functionality and showing how changes have affected the system’s stability. Build such report with testomat.io using a @tags, labels and filtering features for test selection.
9. Compliance Report. In regulated industries, such as finance or healthcare, these reports confirm adherence to relevant standards and may be required for certification.
10. User Acceptance Test (UAT) Report. Includes the results of testing by end-users who evaluate whether the product meets their needs. The report captures user experience, identifies issues, and assesses the readiness of the software for deployment.

We have listed the main types of testing report protocols. Given this classification of testing types, there are many more.

Thus, a test report in software testing is a comprehensive process, where different types of reports address the needs of specific stakeholders and pursue various goals. Each type of report provides specific information on particular aspects of testing, offering a complete picture of the development status and enabling an assessment of the software product’s quality at each stage.

When Should a Test Report in Software Testing be Prepared?

Reporting should occur at regular intervals. For example, ISTQB defines the following concept,

Test Status Report

A document summarizing testing activities and results, produced at regular intervals, to report progress of testing activities against a baseline (such as the original test plan) and to communicate risks and alternatives requiring a decision to management.

The final test report is usually prepared at the concluding, sixth stage:

At each stage, testers analyze their actions in detail to assess the overall quality of the software. Also, a test report in software testing is prepared upon request by stakeholders or to achieve specific goals, as well as after significant software updates.

Use the report to document lessons learned from the test cycle, such as process improvements, issues encountered, and areas where the team can improve for future cycles. This helps drive continuous improvement.

Mikhail Bodnarchuk
CDO, ZappleTech Inc.

What Main Components of Test Report in Software Testing?

Of course, creating a good test report requires a systematic approach that includes several important sections. Each section has a specific purpose and contributes to its completeness.

Let’s explain now the main elements of a test report:

Test Report Introduction

This section serves as an overview of the entire document, providing a summary of the report and setting expectations for readers. The main components of the introduction are:

• Purpose. Clearly indicate whether this is a test summary at a specific stage, an update on progress, or an assessment of the software product’s readiness.
• Date. It is when test execution is run.
• Scope. Outline the scope of testing (sets of test suits and test cases), specifying which aspects of the software were tested, what types of testing were conducted (e.g., functional testing, performance testing, security testing), and note any limitations.
• Software. Specify the particular software or module tested, including software versions and other relevant parameters.

Test environment

This section describes the conditions and setup in which testing was conducted. Key elements:

• Hardware. List of hardware, including specifications (e.g., processor, RAM, etc.), used for testing.
• Software. List of software components, operating systems, versions, and configurations.
• Configurations and versions. Includes network settings, permissions, and software versions.

Test execution overview

This section provides a test summary report, which includes:

• Number of tests. All planned and executed tests.
• Passed and failed test cases. Information on the number of successful and failed tests, along with brief explanations or extract of code if it is automated tests.

Detailed results of testing activities

This section describes each test case

• Test case id and action description. A unique number and a brief description.
• Status and defects. Provides information on the status of each test case, including execution results and identified issues.
• Test data. Information to ensure reproducibility.
• Screenshots or attachments. Adds context and confirms testing outcomes.

Defect summary

This section analyzes defects:

• Total number and categories. Specifies the number and categories based on severity and priority.
• Defect status. Link to test cases and status of defects (e.g., open, closed).
• Actions for resolution. Describes actions taken to fix defects.

Test coverage

Shows which components of the software were tested and which were not. Key elements:

• Functional areas. List of tested modules.
• Code coverage percentage and uncovered areas. The percentage of code tested during the process and reasons for omissions.

Test execution summary and recommendations

A summary of key results and recommendations:

• Testing results and achieved goals. This is an overall assessment of the test results and progress toward achieving the test objectives.
• Areas for improvement and suggestions.  This section provides recommendations aimed at improving the software quality and the testing process.

These parts of the report help to give a comprehensive view of the test results and create a basis for further steps in software development.

Look at the other more advanced test report template that you can easily create using Google Doc tables.

Step-by-Step Guide:
How to Create an Effective Test Report in Software Testing?

The report is an important communication tool between all participants in the development process, as it provides critical information about the quality and progress of software testing.
Thus, creating a comprehensive and informative report on the testing activities requires clear and structured organization to ensure clarity and accuracy.

Here is a step-by-step guide on how to prepare a complete and effective report:

#1: Defining the Objective

Before preparing the report, clearly define its purpose and consider who the target audience will be. Determine which data is necessary to make an informed decision. For example, for management, the overall progress is important, while for testers, the details of some particular test results are crucial.

#2: Collect Detailed Data to generate report

To prepare an accurate test report in software testing, it is essential to record all critical testing metrics, such as results, defects, environment settings, and other relevant parameters. Test management tools can be used to gather data automatically.

#3: Selecting Relevant Test Report Metrics

The choice of testing metrics, such as test execution time, number of defects, test coverage, and progress of test case execution, should be aligned with the needs of the target audience of the report to effectively assess software quality.

#4: Simplicity of Presentation Test Result

It is important that the information is presented in simple, understandable language, without excessive technical jargon that may be difficult for individuals without technical expertise.

#5:  Data Test Report Visualization

Graphs, charts, and tables help effectively present testing results. They can show the status of each test case, defect trends, or comparisons of results across different test cycles.

#6: Adding Context and Analysis

Additional explanations for visualizations are important for understanding trends, anomalies, or critical defects. Brief comments help interpret the data better.

#7: Review and Editing

The report should be reviewed for errors and logical inconsistencies. It is crucial to ensure the accuracy of the information and consistency of conclusions with the facts presented in the test report in software testing.

#8: Automating Report Generation & its sharing

Consider the possibility of automating report creation using specialized tools that integrate with testing platforms. This will allow data collection and the automatic generation of standardized reports. Also, ability to share report fast and for free with colleagues is a great benefit.

Automated Test Report Generation

Automated testing has become an established trend in the software testing industry. It involves using specialized tools to run tests with minimal human involvement, analyze the results, and create reports based on the test outcomes.

This type of testing is an integral part of every Agile team, helping meet the requirements for fast yet high-quality software products. Automation allows teams to improve productivity, identify bugs more effectively, and much more.

The development of automated testing significantly eases the work of testers and quality control professionals, saving time and effort during basic testing and repetitive tests.

Automated testing reports are a crucial element of the automation testing of your App. After executing automated test suites, the results become the primary for analysis. They provide information that helps determine whether the product is ready for release.

Modern test management platforms like testomat.io simplify report generation, enabling you to track the progress of quality assurance in real-time and evaluate results after each testing cycle. The tool optimizes the testing process by integrating all necessary functions into a single interface. It provides management and analysis of test reports by centralizing data from both manual and automated tests in one place. This integration makes it easier to monitor and control the testing process effectively.

Testomat.io Capabilities in Testing Reporting

The tool provides up-to-date data immediately after test completion. Testing progress is displayed using pie charts, execution curves, indicators, and heatmaps, allowing quick prioritization of tasks for developers and testers.

Run grouping tests and representation of their results performing in-depth analyses to forecast future stages.

Archive grouping. Storing test runs that enable tracking data history.

This test management system allows unlimited Artifact storage of videos and screenshots in any popular cloud storage, regardless of whether you are testing on your personal computer or specialized CI\CD platforms.

Note, that we implement an in-built Playwright trace viewer feature especially cool

Automatic notifications after test completion, results can be sent via email, Slack, MS Teams, or Jira included.

Public reports. Stakeholders don’t need to create an account to access test results – they can share a common HTML report.

Advanced deep analytics. The dashboard includes widgets for analytics, allowing you to track automation coverage, number of defects, test types (Ever-Failing, Slowest, Flaky, Never Run Tests), and links to Jira Issues.

BDD tool support allows writing test scenarios using Gherkin Syntax and automatically converting standard tests into the BDD format. Additionally, a History tab is available for analyzing version changes in scenarios.

Full Jira integration. With the Jira plugin, collaboration between developers and the testing team becomes easier: you can link defects to Jira Issues, send bug reports, and track bug fixes.

Finally, our win tool is your reliable assistant in creating high-quality software products through test automation, ensuring greater openness and transparency in team workflows.

You can meet all its functionality by following the link All Test Management features

CI\CD Test Automation Report

As software development evolves, test automation becomes increasingly important for enhancing efficiency and quality. This is particularly true for CI\CD, where automated testing has become a core element of the CI\CD process. Automation testing ensures reliable fast verification of code changes before deployment. What about CI\CD, it helps maintain high quality and stability of the software, makes development more efficient, and minimizes the likelihood of errors.

Together CI\CD automation, updates occur more frequently, while development environments are configured to ensure that issues are detected and resolved promptly.

Test management system testomat.io can be used to create a test report while tests are runner on CI\CD, helping to effectively organize and automate software testing process.

Best Practices for Creating a Test Report in Software Testing

Follow these recommendations:

1. Adapt the level of detail in the reports to the needs of stakeholders to provide the information they require.
2. Focus on achievements and improvements since the last report to show progress and boost the motivation of the testing team.
3. Identify risks and issues encountered during testing, and offer solutions to resolve them. This ensures transparency and a proactive approach to problem-solving.
4. Compare current test results with previous ones to observe progress and identify opportunities for improvement.
5. Base the report on accurate and verified data. Use cross-references to verify the information.
6. Reports should be team-friendly and promote knowledge sharing among all team members.
7. Highlight valuable insights at the beginning of the report, so stakeholders can quickly familiarize themselves with the most important data.

By following these approaches, reporting will become more effective and user-friendly

Challenges in Reporting During Continuous Testing

Reporting in the testing process can be difficult, especially when test results contradict one another. To produce reports that are both accurate and useful, the testing team must carefully collect data and employ effective tools for presenting it.

Manual test reporting during software testing is labor-intensive and can delay the software development life cycle. Converting test results into clear, understandable metrics and reports for stakeholders often becomes a challenging task.

Main challenges encountered:

1. Information overload. In large testing projects, creating detailed reports can result in an excess of data, making it challenging to emphasize the most important insights for stakeholders.
2. Report accuracy. The reliability of the reports is heavily influenced by the stability and completeness of the test data. Inaccuracies or missing details can undermine confidence in the reporting process.
3. Complexity of interpretation. Reports may be difficult to interpret, particularly for individuals lacking deep technical expertise. It is important to ensure clarity and accessibility when presenting results.
4. Time constraints. Preparing detailed reports requires significant time and human resources, which is a challenge for fast and frequent test cycles.
5. Low quality and traceability. When test reports, defects, and requirements are stored in different places, project managers find it difficult to make an objective assessment of the build quality and its readiness for release.
6. Limited disk space. Although formats like PDF, HTML, or CSV are practical for one-time use, their long-term storage can rapidly occupy substantial hard drive space.

Addressing these challenges can greatly improve the exchange of information about test results and progress, facilitating quicker detection and resolution of software issues.

Conclusion

A test report is an important tool for monitoring the state of the software and taking corrective actions to improve its overall quality. Well-structured test reports support progress tracking, early identification of issues, data-driven decision-making, adherence to standards, and smooth communication within the team. It also supports team collaboration, ensures a shared understanding of tasks, and fosters transparency, which is critical for complex software development.

The post Test Report in Software Testing appeared first on testomat.io.

Determine desired performance during the planning stage and compare them with actual results obtained by the testing team.

– Do the numbers match?

Congratulations!

– Your development and testing efforts will allow you to create high-quality software products.

Are the results obtained far from the standard ones?

– Test effectiveness on the project requires some improvement…

Today, we will discuss the Main Software Test Metrics used in the QA process in modern Agile teams.

Also read to this subject:

• Key Software Testing Metrics & KPI’s
• What are Quality Gates and How Will They Help Your Project?

Test Metric Concept

What is Test Metrics in Software Testing?

Software test metrics quantify various aspects of the test process, including productivity, reliability, and effectiveness. It is advisable to use them at all stages of the Software Development Life Cycle (SDLC), as this will allow you to identify areas for improvement, make informed decisions, and achieve the desired software quality.

The Stages of Quality Metrics in Software Testing

Each of the base metrics in the process of developing a digital solution goes through a particular life cycle, which consists of four stages:

• Analysis. The first step is to select the right metrics used on the project and determine the desired results.
• Communication. Ensuring all process participants understand why and how to use this metric is essential.
• Quantification. At this stage, an assessment of the testing process is performed according to selected criteria.
• Drawing up reports. Once the assessment results are received, a detailed report should be compiled and distributed to the test manager, members of the QA team and the dev team. Regardless of the type, these test metrics’ life cycle stages are common to all metrics. What are they like? Let’s consider the ones below

Classes and types of metrics in software testing

All indicators used for software test measurement are traditionally divided into four classes, depending on which aspect testing efforts help to evaluate:

#1. Process metrics for testing in software engineering

Allow us to draw conclusions about QA process efficiency and indicate to the testing team which part of the software testing process needs improvement.

#2. Product testing metrics in software testing

These are metrics that provide information about product quality. For example, you can consider the defect count or the results of performance testing.

#3. Project metrics in software testing

Such identifiers allow you to evaluate the effectiveness of the testing process. Define test automation or test execution coverage, track fixed defects percentage, and you will understand what your project is missing.

#4. People test metrics in software testing

These indicators help assess the qualifications of an individual test team member. For example, you can determine how many bugs account for one specialist or the percentage of defects identified by the tester.

#5. Leading and lagging software testing metrics

Leading and lagging software testing metrics are also distinguished. Each of them has its own role, but both types are equally important for the team. The former (for example, test coverage percentage or testing status) allow timely actions to be taken to eliminate bottlenecks. The latter (for example, defect leakage) help draw conclusions about the quality of the QA process and avoid repeating the same mistakes in the future.

There is one more way to systematize metrics. Among all existing software testing metrics, absolute and derivative metrics are distinguished:

• Absolute metrics
• Derivative metrics

#6. Absolute software testing metrics

These are absolute numbers, which are easy to measure: for example, the number of bugs found, total number of requirements and number of requirements covered, number of test cases, and quantity passed and failed test cases.

#7. Derivative metrics

Derivative metrics are indicators obtained as a result of combining several absolute metrics. For example, you can calculate the percentage of all and passed test cases, test coverage, defect density, etc.

Let’s look at both types of software testing metrics in detail

Absolute Software Testing Quality Metrics as a Basis For Software Test Measurement

Measured absolute numbers that can be used to calculate work-relevant testing team metric include:

→ total number of test cases written by the QA team;
→ Quantity passed, failed, blocked test cases;
→ number of defects – found, critical, accepted, rejected, deferred;
→ planned time period on software testing and actual number of test hours;
→ number of requirements for software products;
→ total number of bugs detected after delivery;
→ test execution time.

This is not a complete list of absolute software testing metrics used in modern automated and manual testing. All of the above indicators have one thing in common: they can be used to calculate derived metrics, which show an accurate picture of the condition of the QA process on the project.

Derivative software metrics in software testing and methods for their calculation

Obtaining derived metrics, or calculated software testing metrics, is not as straightforward as collecting absolute numbers – you need to use specific formulas. We list the most commonly used indicators and methods for calculating each.

QA Metrics in Software Testing, Which Measure Testing Effort (Productivity Metrics in Software Testing)

These software testing metrics can be attributed to the following:

In this formula:

• Total number of tests is the total count of tests executed.
• Actual time spent testing is the duration of time over which the tests were run.

If you want to express this in terms of tests per hour, you can specify the unit of time accordingly. For example, if the actual time spent testing is in hours, the result will be the number of tests run per hour.

In this formula:

• Number of test cases is the total number of test cases developed.
• Time spent developing is the total time spent on test design and developing those test cases.

This will give you the number of test cases developed per unit of time (e.g., test cases per hour).

In this formula:

• Number of tests reviewed is the total number of test cases reviewed.
• Time spent reviewing is the total time spent reviewing those test cases.

This will give you the test review efficiency, or the number of test cases reviewed per unit of time (e.g., tests reviewed per hour).

In this formula:

• Total number of bugs is the total count of defects or bugs found.
• Total number of tests is the total count of test cases executed.

This will give you the average number of defects found per test case.

Indicator That Allows You to Evaluate Test Effectiveness

These metrics demonstrate the quality of the test set. In other words, it shows how many bugs you can found using test cases. It is calculated using the formula:

In this formula:

• Number of defects detected with one test is the number of defects found during testing.
• Total number of bugs is the total count of defects detected during the testing phase.
• Number of bugs found after release is the number of defects discovered post-release.

This formula calculates the percentage of defects detected during testing relative to the total number of defects (both during testing and after release).

Knowing the information about defect detection, you will be able to calculate another metric. Defect leakage. This is the number of defects that were missed during testing.

Test Coverage Metrics in Software Testing, or Progress Metrics in Software Testing

Test coverage means the amount of testing performed by a particular test set. Test coverage metrics measure:

In this formula:

• Number of completed test runs is the count of test runs that have been executed.
• Number of planned test runs is the total count of test runs that were planned to be executed.

In this formula:

• Number of Test Cases Automated is the number of successfully automated test cases.
• Average Time Saved per Test Run is the time each test case would have taken vs automated time.
• Total Automation Effort is time spent creating, debugging, and maintaining the automated tests.

This metric will show you how efficient your automation efforts are.

Indicator That Allows You to Evaluate Test Case Effectiveness: Defect Metrics in Software Testing

This formula gives you the percentage of planned tests that have been completed.

In this formula:

• Number of requirements covered is the count of requirements that have been successfully covered by test cases.
• Total number of requirements is the total count of requirements specified for the project.

This formula gives you the percentage of requirements that are covered by test cases.

Test Economics Metrics

Such test metrics allow the test team to estimate the cost of testing, stay within the project budget, correctly plan the choice of infrastructure and tools, and determine the required number of quality assurance specialists. These include:

• Total Allocated Costs – budget approved by QA directors on test activities within a specific project or time interval.
• Actual cost – the amount spent on the test process. To calculate this metric, use the data about expenses per test hour. Per test hour, test case, or requirements.
• Budget Variance – the difference between planned and actual expenses for the testing process.
• Time Variance – the difference between planned and actual time testing.
• Cost per bug fix – error correction cost per team member.
• Cost of not testing – price of actual rework efforts spent due to insufficient testing of new functionality.

Test Team Metrics

These indicators reflect the uniform load distribution on each team member and the work efficiency of the QA team. It is advisable to calculate:

• Quantity of defects returned per team member;
• Quantity of valid defects, which are subject to re-testing by each team member;
• Number of test cases assigned to one specialist;
• Quantity of test cases executed by team members.

Scroll software testing metrics may vary depending on your project’s characteristics and goals. Choosing metrics that meet your needs is essential for the right planning and implementing test processes and achieving desired results. Next, we will discuss the correctness test metrics in QA.

What are Software Metrics in Software Testing?

We have mentioned a large number of indicators that can be useful for QA teams. To summarize everything said above, let’s list the key questions that you should answer based on the data you have collected:

— What is the testing status of the project? This refers to the progress made (e.g., number of completed tests, time spent, etc.).
— How many defects have been identified and resolved? Answering this question will help you assess the quality of the software product, the effectiveness of the QA engineers, the speed of issue resolution, and the quality of communication between teams.
— Will we be able to meet the deadlines? It is crucial for a project to adhere to the established deadlines. Any delay in SDLC stages can lead to budget overruns.
— Are we within the established financial framework? We already mentioned this in the previous point. If the team is unproductive or there are gaps in processes, your budget may start to fall apart.
— What are our bottlenecks? The more indicators you use, the more aspects of the QA process you can assess. Therefore, it is important to approach the selection of appropriate evaluation criteria with great responsibility. This will be discussed further.

How to Select the Right Software Testing Metrics?

When choosing metrics for testing process assessment, it is necessary to pay attention to specific criteria:

1. Test metrics should be easy to measure. All metrics discussed in this article are easy to calculate. For example, let’s say we need to determine the percentage of critical defects on a project, even though the general number of defects is 45, and the critical ones are 10. Using the formula given above, we get: (10 / 45) * 100 = 22 %
2. Correct QA metrics are easy to update according to project changes. This is especially true for automated testing, which requires regular test updates.
3. Correct testing metrics help you achieve your business goals. For example, if cutting your budget is essential to you, consider test economics metrics. Teams looking to speed up future releases should focus on assessing test effectiveness and productivity of testing teams.

Determine the indicators you need at the very beginning of a project. This will allow you to optimize your testing efforts, stay within budget, and meet deadlines.

How to choose Test Metrics and Measurement in Software Testing

Use our tips to choose optimal metrics for your testing process:

• Consider the type of testing that predominates in your project. For example, if you test a digital solution manually, it makes sense to focus on tracking defect metrics. If most of your tests are automated, pay attention to the percentage of passed test cases or actual test hours number.
• Agree on testing metrics with the development methodology. Most teams use the agile development methodology. It makes sense for them to concentrate on the number of defects identified after the next sprint or at production. If your team works on a waterfall model, test coverage metrics and other indicators will be critical for you.
• Use metrics important to everyone. Tracking test coverage or a number of defects is indispensable for the QA and development team; however, this data will not be very informative for the customer. So, balance the metrics indicative for the team with test economics metrics or indicators that show time for future releases.
• Limit the amount of test metrics you see. Don’t try to control everything at once. Even at the planning stage, determine the most significant indicators to you and track them throughout your work on the project.
• Share responsibilities. Every test team member must understand which indicators are in their area of ​​responsibility. The project manager can control test coverage metrics or team productivity, and the QA lead can take over tracking defect removal efficiency and similar metrics that will help in future test planning.

Such a thorough approach to tracking key testing metrics will benefit your team, which we will discuss next.

Benefits of Using Metrics and Statistics in Software Testing

Teams that track important metrics on their projects notice clear improvements in the testing process, namely:

• Increase of testing system’s efficiency. Systematic tracking of selected test metrics allows the team to identify areas of improvement and optimize the testing process.
• Quality improvement of the software product. Metrics help identify bugs in the early stages of development and ensure the high quality of the final solution, which contributes to increased end-user satisfaction.
• Reducing risks by project. Analyzing reliable data obtained by tracking testing metrics allows you to predict testing progress, identify problems promptly, and thus effectively manage existing risks.
• Continuous improvement of the testing team. Tracking metrics allow the team to draw conclusions regarding their productivity and determine the factors that provoked the occurrence of bottlenecks. Such work process analysis will enable you to constantly look for new work methods and tools if you obtain unfavorable results.
• Identifying problems at an early stage of the SDLC. It is well known that fixing errors at the beginning of development is much cheaper than when a bug is discovered in production. Accordingly, the more carefully you monitor the indicators accepted on the project, the higher the likelihood of noticing early on that something is going wrong.
• Meeting deadlines. What do you think are the metrics that contribute the most to this? Correct, progress metrics. You can monitor step by step whether you are adhering to the planned schedule or if your progress has slowed down too much.
• Adequate resource allocation. Knowing the status of your project in numbers is useful for another reason. This way, you can understand which part of the product requires the most attention and direct the lion’s share of resources there. This will allow you to focus the most on critically important features and address the most serious bugs on time.

Thus, software testing metrics allow you to determine the level of testing process efficiency based on quality data, software product, and team productivity. If tracking metrics show that you have not achieved the desired results, change your testing strategy, try new tools, and work with your team. This testing approach will optimize your test process on the project and help you create high-quality digital solutions.

Final Thoughts: What is Metrics in Software Testing

QA metrics are indicators that allow teams to ensure everything is going as planned. They help track the status of testing, team productivity, the effectiveness of QA strategies, and other aspects of the SDLC.

To make working with these indicators effective, take their selection seriously, and choose a reliable tool with comprehensive analytical capabilities.

For example, the Test Management System testomat.io provides users with convenient widgets for tracking various metrics. Among them are Test Execution, Defect, Test Coverage, Automation metrics, and many others.

Take advantage of the platform’s features and improve the efficiency of your QA processes!

The post Detailed Guide to the Basics of Software Testing Metrics appeared first on testomat.io.

Software testing metrics are a way to measure quality and monitor the progress of your test activities. Test metrics give us better answers than just we have tested it.

What are the benefits of QA metrics?

• QA testing metrics gives us knowledge: How many bugs found were fixed? reopened? closed? deferred? critical among them?
• Defect metrics in software testing diagnose problems, help verify and localize them.
• Help identify the various areas that have issues in the software testing process and allows the team to take effective steps that increase the accuracy of testing.
• Predict risks in our testing as an early warning of an unfavourable result.
• Give an understanding of what exactly needs improvements in our testing strategy to optimize processes.
• Allow us to make the right decisions.

Eventually, testing metrics help validate is your product meets customer expectations

To measure the velocity, efficiency and relevance of these, two types of software testing metrics exist:

→ Indicators.

→ The KPIs.

The difference between KPIs and indicators

The 𝗞𝗣𝗜s are derivative trends calculated from the indicators (fundamental absolute numbers) of concrete results of the test campaign. Different QA teams measure various aspects within their testing depending on what they want to track, control or improve. Note, you can determine the set of indicators over the week, month, year or another period.

You cannot improve what you cannot measure.

Presented below test metrics in software testing allow you to quantify the different scopes and actions contained in the latter. Here is a non-exhaustive list:

Indicators:

1. Number of requirements: This is the overall perimeter of the change.
2. Number of requirements tested: This indicator lets you know the number of requirements with at least one test case.
3. Total number of test cases: A test case corresponds to a verification scenario of the acceptance criteria of a requirement.
4. Number of test cases run: Number of test cases executed at least once.
5. Number of designed test cases: The number of test cases built, not reviewed and not executed.
6. Number of reviewed test cases: Number of test cases built, reviewed and not executed.
7. Number of successful test cases: Number of test cases built, reviewed, and executed successfully.
8. Number of failed test cases: Number of test cases built, reviewed, and executed but which detected a bug.
9. Number of blocked test cases: Number of test cases built, reviewed, but that cannot be executed for a technical or project reason.
10. Total test design time: This is the total time it took to design and write the test cases.
11. Total test reviewed time: This is the total time it takes before playing the tests, it is always relevant to have them validated by peers (whether for developers or testers).
12. Total test design time: This is based on the estimates made during the change preparation phases for all the test cases planned for each campaign.
13. Total test execution time: It represents the actual test execution time.
14. Number of defects found: This represents the total number of defects found by the tester.
15. Number of accepted defects: Here, it concerns the defects previously found and validated or not by the developers or the project managers.
16. Number of rejected defects: This is the delta between the faults detected and those which are validated.
17. Number of deferred defects: These feed the backlog of defects that will not be corrected during the iteration phase (whether by priority, by criticality or for a strategic reason).
18. Number of critical defects: This one allows you to measure the quality of the change because it highlights the number of critical bugs.
19. Number of resolved defects: The latter makes it possible to quantify the number of defects resolved during the iteration phase. The defects created vs. Resolved chart shows the rate of defect fixing. This grabs the team’s attention if this rate is slower than the rate desired.
20. Total default resolution time: This allows you to measure the cost of bugs and their resolution.
21. Total default review time: A resolved defect being tested, there is an additional time and an impact on the velocity of the team which can be measured thanks to this indicator.
22. Number of defects found after delivery: Finding a bug in production is relatively critical, this indicator will be a good measure. The lower it is, the higher the retention rate.

Mostly we visualize indicators through tables and line charts. See a few examples below:

Note: Indicators do not measure the quality of your applications. Absolute numbers are a great starting point, but typically indicators are not enough alone. Only KPIs are reliable and relevant elements to address this issue.

Typically QAs make calculations of Software Test Metrics with XLSX tables. Below are represented indicators prepared for it.

The value of each indicator depends on the project SDLC phase in which results are outputted.

At the same time is critical to determine for your testing:

• the project needs
• the goal for software testing metrics
• the appropriate testing metrics
• the target consumers of metrics

Taking these ones into account, here is a non-exhaustive list of the different KPIs that you can put in place:

→ Monitoring and test efficiency

→ Test effort per campaign

→ Test coverage

→ Cost of testing

Let’s explain these test software testing metrics

Monitoring and test efficiency metrics

Business Objective: Obtain the Pass share of the executed test cases to evaluate quality in general.

The percentage of successful test cases provides an indication of how well a software product performs against its desired outcomes. The higher indicator, the higher quality. This metric, in turn, can be used to compare different App versions.

Business Objective: It is a strong indicator of the product bug rate.

The percentage of failed test cases metric helps identify the software’s readiness for delivery. A high ratio indicates critical issues of quality. If you have a ~ 50% failure rate, it shows terrible requirements definition. Vise versa low bug rate is essential for businesses to ensure customer satisfaction and maximize profits.

Business Objective: Indicates how developed software features work properly.

It is a metric used to measure the percentage of test cases that can not be executed because of some impediment such as missing prerequisites, unavailability of the test environment, unavailability of required test data, or a defect in the system under test.

Business Objective: Ensure the overall quality control of the development process.

This metric is used to measure the effectiveness of a software development team in resolving defects found during testing. A low fixed defects percentage indicates the possibility of delays in project delivery.

Note: Fixed defects percentage is often used in conjunction with other software development metrics, such as defect density and defect backlog, to provide a more comprehensive view of the development process and to identify areas for improvement.

Business Objective: Represent the robustness of the defect management process.

A high Percentage of Defects Rejected indicates that team has a robust defect management process and is successful in detecting and fixing defects early in the development cycle, reducing the risk of defects escaping to production. A low PDR indicates that the team needs to improve their testing practices.

Business Objective: This metric helps understand the effectiveness of QA management processes as well.

The percentage of deferred defects identifies defects that were deferred within a particular time frame, sprint or release cycle. A high percentage of deferred defects may indicate a need for improvements in the development or testing process, or a lack of resources to address all identified issues within a given timeframe.

Business Objective: Measure the severity of defects found during software testing

Because this metric is useful for reducing the number of critical defects before the software is released to production.

Business Objective: Measure the average time it takes to resolve reported bugs or issues.

A high average bug resolution time may indicate that the team is struggling to fix bugs in a timely manner, which can lead to delays in software releases and potentially impact customer satisfaction.

It’s worth noting that this metric should not be the sole indicator of a development team’s performance. Other software test metrics, such as the number of bugs reported, the severity of bugs, and customer satisfaction ratings, should also be considered to gain a more comprehensive understanding of the team’s effectiveness.

The percentage ratios are convenient to depict with the pie charts. An example is generated by Test Management System automatically.

Test effort per campaign

Business Objective: This test metric provides insight into how efficiently the testing process is being conducted, so helps optimize our testing time. Measuring the efficiency of the test case execution gives us a time frame of how much time is spent executing each test case.

The TEE (Test Execution Efficiency) metric includes any time spent waiting for resources, such as databases or network connections, to become available. A high TEE value indicates that the testing process is efficient, with minimal wasted time and effort, while a low TEE value may indicate inefficiencies that need to be addressed. For instance that there are too many test cases to execute, or the testing environment is not set up properly. Also, it can help prioritize test cases and allocate resources accordingly.

Also, TEE can be used as a metric to track progress over time. By comparing TEE from one testing cycle to another, teams can see whether improvements have been made and identify areas for further optimization.

Business Objective: This metric provides insight into how much time is being spent on designing each individual test case.

Test design efficiency is useful to optimize resource allocation. By knowing how much time is required to design a single test case, you can better allocate resources to the test design process. For example, to set realistic expectations for stakeholders. If you know that it takes an average of 30 minutes to design a test case, you can plan accordingly and ensure that you have the necessary resources available to complete the work within the desired timeframe.

It can also be used to identify areas that may be slowing down the process for improvement in the test design process. By tracking the progress and performance of Test design efficiency over time, you can monitor progress and identify trends.

Business Objective: Test review efficiency refers to how efficiently a team or individual is able to review tests.

The test review efficiency metric can help teams identify areas where they can improve their testing process, such as streamlining review procedures or identifying bottlenecks that can be addressed more efficiently.

For example, you may discover that certain types of tests take longer to review, allowing you to adjust your testing procedures accordingly and reduce overall testing time.

Also, measuring test review efficiency can help QA managers allocate resources more effectively. By understanding how much time is required for test review, managers can determine how many resources (i.e., people, time, and money) are needed to complete the testing process to meet deadlines.

Business Objective: The default rate per test time evaluates the overall efficiency of a testing process, as it provides insight into the number of defects detected within a specific time frame.

The numerator of this formula represents the number of defects or faults detected during the test period, while the denominator represents the total time taken to execute the tests.

It is important to note that this metric should be used in conjunction with other testing metrics, such as defect density or test coverage, to get a more complete picture of how a testing process goes.

Business Objective: This metric provides an estimate of how many defects were found on average per test case execution.

It is worth noting that the accuracy of this metric can be influenced by factors such as the quality of the test cases, the complexity of the software under test, and the expertise of the testing team. Therefore, it’s important to interpret the results in the context of the specific testing environment and to use this metric in conjunction with other test metrics to get a comprehensive understanding of the effectiveness of the testing process.

Business Objective: The Average bug resolution time is a test metric that measures the average time taken to resolve a software bug or defect from the time it was identified until it was successfully fixed.

Here, the total fault resolution time is the sum of the time taken to fix each bug, and the number of faults resolved is the total number of bugs that were fixed during the testing process. The number of faults deferred is the total number of bugs that were not fixed during testing but were instead deferred to a later stage.

By calculating the average bug resolution time, testing teams can monitor their bug-fixing progress. This can help them optimize their processes, allocate resources more effectively, and improve efficiency. A lower average bug resolution time indicates that bugs are being fixed quickly, which can help improve software quality and reduce the overall testing time. On the other hand, a high average bug resolution time that is taking longer than expected can indicate problems in the software development process or the need for additional resources or expertise. If the average time to fix a bug is increasing over time, it may indicate that the testing process is becoming less efficient or that the software is becoming more complex and difficult to test.

The average bug resolution time can also be used to set expectations with stakeholders, such as project managers or customers. Providing an estimate of how long it will take to fix bugs, it helps manage expectations and ensure that stakeholders are aware of the time and resources required to resolve issues.

Business Objective: This test metric determines the overall quality of the software product.

This formula calculates the percentage of defects that were identified and resolved during the testing process, compared to the total number of defects that were found, including those that were discovered after the software was released to users.

The higher the percentage of resolved defects, the more efficient the testing process is in identifying and resolving defects before the software will release. This means that the software is more likely to meet the requirements of the stakeholders, have fewer bugs, and be more reliable. On the other hand, if the percentage of resolved defects is low, it indicates that the testing process may not have been thorough enough or that there were issues with the software development process, which may have led to a higher number of defects being found after release.

Test coverage

Business Objective: Requirements coverage should complete 100% by tests

With the assistance of this key performance indicator, the team can track the percentage of requirements covered by at least one test case. This metric helps evaluate the functional coverage of test case design. The QA manager monitors this KPI and specifies what should be done when requirements cannot be mapped to a test case.

Business Objective: Show what the software product’s functionality is validated.
It helps us get an idea about the total number of test cases executed as well as the number of test cases left pending. Everyday test manager monitors this KPI to ensure the main program functionality coverage.

Business Objective: Shows the readiness of the product to release
If a critical requirement has yet to pass testing, the release should be delayed.

Business Objective: Controls that we do not miss anything important
This KPI is to make sure that any functionality that the testing will not be missed by the QAs team.

Cost of testing

Estimated cost Test: Estimated total test execution time + Total test execution time (expressed in hours) x Hourly cost of a tester.
Business Objective: The threshold testing cost.
Software quality assurance can be quite expensive. Provide a good return on your investment.

Average cost of testing per requirement: Estimated cost Test / Number of requirements tested.
Business Objective: Controls that we do not miss anything important
This KPI is to make sure that any functionality that the testing will not be missed by the QAs team.

Actual campaign test cost: Estimated Total Test Execution Time + Total Test Execution Time +total replay time (expressed in hours)x Hourly cost of a tester
Business Objective: Controls that we do not miss anything important
This KPI is to make sure that any functionality that the testing will not be missed by the QAs team.

Total campaign cost: Actual campaign test cost + (Total defect resolution time x Hourly cost of a developer)
Business Objective: Сontrol of testing сosts
Our budget should match testing costs. And one of the main manager’s tasks is to track it.

Cost Hourly difference: Total campaign cost – Estimated cost Test
Business Objective: Controls that we do not miss anything important
This KPI is to make sure that any functionality that the testing will not be missed by the QAs team.

Cost of bugs: (Total defect resolution time x Hourly cost of a dev) + (total replay time x Hourly cost of a tester)
Business Objective: This is calculated by the dollar amount of effort spent on a defect.
This KPI is to make sure that any functionality that the testing will not be missed by the QAs team.

Wrap-up:

This material describes the basic metrics and showcases how you can transform them for valuable insights.

Overall, analyzing your current system by using software testing metrics can help you understand how much your testing strategy and processes are optimized and which areas need improvement. As a result, you’ll be able to make wise decisions for the next phase of the development process.

On the other hand, software testing quality metrics are a funny thing. They can be used for good or warped for bad. Therefore it is very important to focus on the software testing metrics themselves and separate the discussion of what the test metrics might mean. Because there is a risk to paint an artificially pretty picture. Be forthcoming in the information and help stakeholders through the repercussions and make the necessary changes. It is essential for improving the efficiency and cost-effectiveness of your testing.

I also know that if the assessment isn’t automatized and available to the QA managers under easy-to-read reports, it might become an intimidating process that gets constantly postponed.

But there is a way out, test management systems and test automation software are used to execute tests in automated testing and calculate test metrics automatically. And it is the contribution to optimizing QA processes as well.

The post Key Software Testing Metrics & KPI’s appeared first on testomat.io.

A Heat Map (or Heatmap) is a graphical 2-dimensional format representation of data where values are depicted by color.

Heat maps make it easy to visualize complex data and understand it at a glance.

In other words, Heatmaps is about replacing numbers with colors because the human brain understands visuals better than numbers, text, or any written data.

In Software Testing color give visual cues about how data are clustered or varies over the testing process.

Heatmaps can describe the density or intensity of variables, visualize patterns, variance, and even anomalies. Heatmaps show relationships between variables.

Color variation gives visual cues to the readers about the magnitude of numeric values.

These Heatmaps are data-driven “paint by numbers” canvas overlaid on top of an image. The cells with higher values than other cells are given a hot color, while cells with lower values are assigned a cold color.

6 interesting facts about Heatmaps:

1. The practice we now call heat maps is thought to have originated in the 19th century, where manual grey-scale shading was used to depict data patterns in matrices and tables. See, original Heat Map of the 19th century, where manual grey-scale shading was used to depict data patterns in matrices and tables.
2. Spectrum rainbow colors are best perceived, so nowadays we can see colored models. Note, good color schemes help you see structure in numeric data. Lighter colors correspond to smaller amounts and darker shades to larger values, or vice versa.
3. The term “Heatmap” was first trademarked in the early 1990s, when software designer Cormac Kinney created a tool to graphically display real-time financial market information.
4. In the Software Testing, the first mention was for prediction of the reliability of large software products, the technique was evaluated on Microsoft Vista and Eclipse.
5. Heatmaps are the most commonly used for studying genomes to represent the level of expression of many genes.
6. Also, Heatmaps are popular in marketing researches and sales. In testing as a website UI/UX testing, A/B testing, Performance testing.

What is a website heat map?

Website heatmaps visualize the most popular (hot) and unpopular (cold) elements of a webpage using colors on a scale from red to blue.

Displays user behavior, heatmaps facilitate data analysis and give an at-a-glance understanding of how people interact with an individual website page — what they click on, scroll through, or ignore—which helps identify trends and optimize for further engagement.

So, website heatmaps are popular to carry out UX/UI testing and A/B testing, Performance testing. Visualize users’ preferred browsing and shopping behavior etc.

The benefits of using heat maps:

• Visualization makes it easy to spot strong dependencies. Yeah, it is essentially a kind of a Real-Time report.
• Shows patterns and data dependencies.
• Display changes over time.
• Heatmaps help locate hidden errors, understand the problematic parts of the product under development.
• Based on these, QA Engineers uses Heatmaps for prioritizing test effort with the early warning.
• Stakeholders for making the right decisions.
• Businesses can improve systems by identifying, monitoring and correcting anomalies.

Summary

Heatmaps represent data in an easy-to-understand manner to communicate to team members or clients. Thus visualizing methods like HeatMaps have become popular.

Testomat.io Test Management System provides Test results Heatmaps by test suite on Test Reports. Heatmaps shouldn’t be used apart. Good practice correlate them to analytics and the context of your research, what we successfully developed into our Test Management tool as well. Rich Analytics is available in Professional, Enterprise and in Free Trial plans

Analytics widgets include:

• Flaky Tests
• Slowest Tests
• Never Run
• Tests Ever
• Failing Tests

…and many more.

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