Test Execution with Test Metrics
The effective execution of tests and the accurate measurement of their outcomes are crucial for delivering high-quality software products.
Test metrics serve as a foundation for evaluating the testing process, providing actionable insights that drive improvements.
Let’s delve into the essentials of outlining test metrics, executing tests, and evaluating results based on these metrics.
Test Metrics
Test metrics are quantitative measures used to assess various aspects of the testing process and the quality of the software.
Here are some essential test metrics to consider:
Test Case Preparation Status
This metric measures the number of test cases that are prepared versus the total number of test cases planned.
It is dedicated to ensuring that the test preparation phase is on track and highlights any delays or issues in test case development.
Its aim is to help in planning and resource allocation, ensuring readiness for the test execution phase.
For example:
Total test cases planned: 150
Test cases prepared: 120
With 120 out of 150 test cases prepared, the preparation status is 80%.
This metric shows that the team is nearing completion of the test preparation phase, highlighting the remaining 20% that needs attention.
Test Execution Status
The Test Execution Status metric tracks the number of test cases executed, passed, failed, or blocked.
It provides a snapshot of the testing progress and identifies any immediate issues that need attention. Its goal is to facilitate daily monitoring and reporting of testing activities, ensuring transparency and accountability.
For example,
Test cases executed: 100
Test cases passed: 80
Test cases failed: 15
Test cases blocked: 5
With 100 executed test cases, we can see an 80% pass rate, 15% fail rate, and 5% blocked.
This breakdown helps the team understand current progress and any immediate issues needing resolution.
Defect Density
Defect Density calculates the number of defects found per unit size of the software module (e.g., per thousand lines of code). It helps in identifying the most error-prone areas of the application.
Its importance is that this metric guides focus areas for rigorous testing and development efforts, improving overall software quality.
For example,
Total defects found: 45
Code size: 10,000 lines of code (LOC)
Defect density: 4.5 defects per 1,000 LOC
Defect Density identifies the defect-prone areas in the codebase.
It guides the focus for more rigorous testing and code reviews in high-density areas.
Defect Severity and Priority
This metric can be used to classify defects based on their impact (severity) and urgency (priority).
It helps prioritize defect resolution efforts.
This metric ensures critical defects are addressed promptly, maintaining the software’s functional integrity.
For example,
Critical defects: 5
High-priority defects: 10
Medium-priority defects: 20
Low-priority defects: 10
This metric helps prioritize the resolution efforts. As a result, it ensures that critical and high-priority defects are addressed first to maintain software stability.
Test Coverage
Test Coverage measures the extent to which the code or functionalities are covered by test cases. It ensures that all parts of the application are tested, minimizing the risk of untested areas.
Its goal is to provide confidence in the comprehensiveness of the testing efforts.
For example,
Requirements: 100
Requirements covered by tests: 90
Test coverage: 90%
Test coverage ensures comprehensive testing of functionalities and reduces the risk of untested areas leading to unexpected failures.
Test Effectiveness
This metric evaluates the number of defects found in testing versus those found in production. It assesses the efficiency of the testing process in identifying defects.
Test Effectiveness is aimed to improve the testing process by reducing post-release defects.
For example,
Defects found during testing: 45
Defects found in production: 5
Test effectiveness: 90%
Test effectiveness measures the efficiency of the testing process. A high effectiveness rate indicates a robust testing process.
Mean Time to Detect (MTTD) and Mean Time to Repair (MTTR)
MTTD measures the average time taken to detect a defect, while MTTR measures the average time to fix a defect.
It is used for tracking the responsiveness of the testing and development teams.
Its help is to improve the overall defect management process, ensuring timely detection and resolution.
For example,
MTTD: 2 days
MTTR: 3 days
This metric tracks responsiveness in defect management.
Shorter MTTD and MTTR indicate a proactive and efficient defect resolution process.
Executing Tests and Measuring Results Based on Test Metrics
Effective test execution involves a systematic approach to ensure that test cases are run accurately and efficiently.
Here’s a step-by-step guide:
Preparation Phase
- Develop detailed test cases based on requirements and specifications.
- Ensure the test environment replicates the production environment as closely as possible.
- Establish the test metrics that will be tracked during the execution phase.
Execution Phase
- Execute test cases according to the test plan.
- Document the outcomes of each test case, including pass, fail, or blocked status.
- Record any defects identified during testing, including their severity and priority.
Measurement Phase
- Gather data on test execution status, defect density, test coverage, and other defined metrics.
- Use the collected data to analyze the effectiveness and efficiency of the testing process.
Evaluating Results Based on Test Metrics
The evaluation phase involves interpreting the data collected from test metrics to derive meaningful insights:
Identify Trends and Patterns
- Examine trends in defect density, test coverage, and other metrics over time.
- Identify patterns such as recurring defects in specific modules or areas with low test coverage.
For example,
Trend Analysis:
Defect density trend shows a peak in one module with 10 defects per 1,000 LOC.
Pattern Recognition:
High failure rate in specific functional areas, indicating need for targeted regression testing.
Assess Test Effectiveness
- Compare MTTD and MTTR and evaluate the time taken to detect and fix defects, aiming to reduce these metrics.
- Ensure that all critical functionalities and code areas are adequately tested.
For example,
Compare MTTD and MTTR:
MTTD and MTTR trends show average detection and resolution times, suggesting areas for process improvement.
Test Coverage Analysis:
Achieved 90% test coverage, with remaining 10% planned for the next testing cycle.
Prioritize Improvements
- Direct additional testing efforts to areas with high defect density or low test coverage.
- Refine and update test cases based on defects found and areas with high failure rates.
For example,
Focus on High-Risk Areas:
Additional testing scheduled for the module with high defect density.
Optimize Test Cases:
Test cases updated based on defect patterns to enhance coverage and effectiveness.
Report and Communicate
- Prepare comprehensive reports on test metrics, highlighting key findings and improvement areas.
- Share the results with stakeholders, providing clear insights into the quality of the software and the effectiveness of the testing process.
For example,
Detailed Reports:
Comprehensive report prepared with metrics, trends, and actionable insights.
Stakeholder Communication:
Results shared with stakeholders, highlighting testing progress, defect trends, and recommended improvements.
Example Summary Report:
Test Case Preparation Status: 80% (120/150)
Test Execution Status: 100 executed, 80 passed, 15 failed, 5 blocked
Defect Density: 4.5 defects/1,000 LOC
Defect Severity and Priority: 5 critical, 10 high, 20 medium, 10 low
Test Coverage: 90%
Test Effectiveness: 90% (45 testing defects, 5 production defects)
MTTD: 2 days
MTTR: 3 days
Recommendations:
1. Increase focus on modules with high defect density.
2. Enhance test cases for areas with low pass rates.
3. Shorten MTTD and MTTR by improving defect tracking and resolution processes.
By systematically outlining, executing, and evaluating based on test metrics, teams can ensure a robust and effective testing process, ultimately leading to higher quality software and satisfied customers.
💖 Do not forget to follow us on Linkedin and Facebook to learn more about software testing and tech news.
💎 Try TestCaseLab for free with a 30-day trial subscription here!
Please share this article with those who may benefit from it.
Thank you!
