It’s a common adage that if something can’t be measured, it can’t be improved. This is why you’ll need a standard or a benchmark against which to compare your results. As a result, you must create some agile testing metrics that are appropriate for your agile projects.

While managing agile projects, you may find yourself wondering if your performance is up to par. You can also be trying to find a way to optimize your workflow and create new goals for yourself.

We’ll focus on test metrics in this article and go over agile testing metrics in depth. The standard for measuring the performance of the software testing process in your agile environment is agile testing metrics.

Software Testing Metrics

Software testing metrics are a way to assess the quality of a piece of software. These test metrics might be either quantitative or qualitative. The efficiency and efficacy of your software testing process can be determined using software testing metrics.

As a QA manager, you must make the best option possible when choosing agile testing metrics for your project or firm. Software testing metrics are used to assess many parts of the software testing process as well as the effectiveness of quality assurance teams.

Qualities of Software Testing Metrics

As a best practice, your agile testing metrics should be a combination of metrics that assess different aspects of your product and quality assurance process.

When choosing your agile testing metrics, keep the following qualities in mind:

1. Gives Insight of the Business Value

At least one software testing statistic should be able to be presented to senior management, and they should be able to understand the return value given by that metric. In another scenario, senior management may believe that the time and effort spent on agile testing metrics is not valuable to the company as a whole.

2. Measure Effectiveness

Choose one or more metrics to help you assess the efficacy of your software testing process. One of these agile testing measures is the percentage of defects found.

3. Measure Efficiency

Even if your software quality assurance method is effective, there may be space for efficiency improvements. Such testing measures include defect category, mean time to find defects, and mean time to remedy.

4. Cost Related

As a general practice, your set of metrics should also have a cost related test metrics.

Agile Testing Metrics
Burndown Charts

Burndown charts are simple graphs that are used to track a project’s progress. These diagrams are used in agile projects where teams break their work into sprints and deliver the product.

The team plans the work that will be done during the sprint and estimates its timeline at the start of the sprint. Sprint burndown charts are used to track the sprint’s progress, such as whether it is on schedule or not.

At the release and iteration level, burn down charts show the rate at which features are completed or burnt down. It helps to visualize the quantity of work that still needs to be done. The burndown chart is used to estimate how much time is left to finish a project.

Burndown charts give you a clear picture if things aren’t going as planned by providing data on the following:

  • Work that has to be done
  • Work has been finished
  • In each iteration/sprint, work is completed.
  • In terms of time, the discrepancy between planned and actual performance.

Any spreadsheet, such as Excel or Google Docs, can be used to construct a burndown chart. Note down your intended dates, the estimated planned effort, and the actual effort expended to finish the activity to construct a burndown chart. The y-axis reflects the amount of remaining work, while the x-axis represents time.

Because all effort is yet to be put in at the outset of the sprint, it is at its maximum. As the sprint approaches its end, the remaining effort required diminishes until it reaches zero at the end.

If the actual line is higher than the effort line, it suggests we worked harder than expected on a task. If the actual line is lower than the effort line, it suggests we did the task with less effort. If the actual line and the effort line cross, it means everything is going according to plan.

If there is a significant difference between the actual and the effort line, it is possible that you did not provide realistic estimations. If you’ve supplied reasonable estimations but your actual line is still primarily above the effort line, it’s possible that your QA team is underperforming. As a good QA manager, you should plan accurately enough such that your real and effort lines in the burndown chart meet.

Percent of Test Case Execution

The measure ‘Percent of Test Case Execution’ shows how far the testing has progressed throughout the iteration or sprint. A passed, failed, or blocked/cannot test case can result in a pass, fail, or blocked/cannot test state.

This measure summarizes the test execution actions. It provides you with information on the QA team’s productivity and the status of testing efforts. Because some test cases take longer to complete, you can’t measure a QA’s efficiency just on these metrics.

At the time of completion of the software deliverable, all test case metrics should have a value of 100 percent. If it isn’t 100 percent, the team should analyze the unexecuted test cases to ensure that no valid test cases are missing.

The percentage of test cases executed does not imply that your specified QA tasks were successfully accomplished. It’s possible that you’ll complete all of the test cases, but there’s still a lot of QA work to be done. This is because, while the QA team may have completed all test cases, there may still be a large number of failed or blocked test cases that must be retested until they pass. The ‘Percent of Passed Test Cases,’ which we shall cover next, is a more helpful metric.

Test Case Pass Rate

Based on the percentage of passed test cases, the test case pass rate demonstrates the quality of the solution. It provides you with a clear image of the product’s quality. The number of passed test cases divided by the total number of performed test cases yields the test case pass rate.

As the project progresses, the importance of this statistic should rise. If the test case pass rate does not improve in following phases, it suggests that the QA team is unable to close the defects for whatever reason. If the test case pass rate falls, the QA team will have to reopen the defects, which is even more concerning.

In both circumstances, the QA manager must work closely with the development team and look into the root reasons. It’s also possible that the developers don’t understand the reports since they’re vague or poorly written, focusing on symptoms rather than the fundamental reason.

Defect Category

Defect category metrics can be utilized to gain insight into the product’s many quality aspects. Functionality, usability, performance, security, and compatibility are examples of possible categories.

You must designate a category to each problem or defect when reporting bugs if you wish to use these metrics in your agile project. A QA manager can utilize these metrics to develop a strategy around a single quality attribute.

If a category has a large number of issues, the QA manager will focus on that category in the next iteration or sprint. If there are additional functional difficulties, the QA manager could suggest that the software requirements specification document be improved in terms of quality and clarity.

Similarly, the QA manager could devote additional time and expertise to testing a certain quality trait.

Defect Density

The number of faults detected in a software product divided by the code size is known as defect density. The measure ‘Defect Density’ differs from the metric ‘Count of Defects’ in that the latter does not provide management information.

The defect density metric can be used to estimate the number of faults in the next iteration or sprint, as well as to reflect the quality of the product being developed. The number of flaws per 1,000 lines of code or function pointers can be calculated.

Defect density has its own set of advantages and disadvantages. Before using these metrics as a benchmark, a QA manager must have a good understanding of them. It is recommended that you utilize a tool to calculate the defect density; otherwise, it will be time consuming.

It’s beneficial for comparing projects that are comparable. However, if the complexity of the code is not taken into account, this measure might be misleading, as different areas of the code have varying degrees of difficulty.

A lower defect density suggests that the product being developed is of higher quality, i.e., there are fewer bugs in the product being tested.

Defect Detection Percentage (DDP)

Another significant agile testing measure to determine the quality of your testing process is defect discovery %. The overall quality of your company’s testing procedure is measured by DDP. It is the ratio of the number of faults discovered during testing to the overall number of defects discovered during that phase.

Note that the total number of defects in that phase include the customer reported issues and bugs too.

A greater defect detection percentage indicates a reliable and effective testing process.

Recap

We looked at the various agile testing metrics in this article. Each testing metric is used to assess a certain quantitative or qualitative feature of the software. The QA manager is responsible for methodically selecting agile testing metrics that deliver the best insight and most return on investment to the company. Testing metrics must be thoroughly studied and analyzed, as there are various aspects that might lead to misinterpretation of agile testing data. Several agile testing metrics, such as burndown charts, percent of executed test cases, percent of passed test cases, defect category, defect detection percentage, mean time to defect, and mean time to fix, can be selected to provide visibility into various process management areas. You may spot problem areas in the effectiveness of your software testing process using these data and design a strategy to improve it accordingly.

For more info: https://www.mammoth-ai.com/testing-services/

Also Read: https://www.guru99.com/software-testing.html

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