In today’s fast-paced software delivery environment, test automation is crucial since it simplifies, expedites, and enhances the reliability of software testing.
Test automation employs software tools to run a great deal of tests repeatedly to ensure an application doesn’t break whenever new changes are added.
It is typically considered an alternative to labour-intensive and time-consuming manual testing. Adopting a test automation strategy is not a task for the timid.
To ascertain whether your business is receiving a satisfactory return on its test automation investment, you must rely on carefully selected metrics that assess the performance (past, present, and future) of your automated testing process.
Automated testing implementation is a manager, and any metrics selected to gauge improvement (such as the ratio of manual to automated testing) must account for particular features of the company, industry, or setting in which they are being employed.
There isn’t a single “universal metrics” report that can be used in every situation.
What Do Software Testing Metrics Mean?
A software testing metric aids in monitoring the effectiveness of a specific software quality assurance task. These are the benchmarks that are used to monitor the activity’s success once the procedure is complete.
Many people believe that test automation takes a lot of time to set up and is costly when it comes to the tools and resources needed to carry out test suites successfully.
Metrics and KPIs related to the test automation report can be useful tools for calculating the return on investment (ROI) of the effort invested in automation as well as identifying important areas for development.
QA Leaders must be able to choose the “right” metrics for automation testing, though, for this to be accurately measured.
Problems with Metrics
The following are the primary difficulties in using test automation metrics:
- What do you learn from the metrics? Metrics related to test automation may present an inaccurate or incomplete view of automation’s effectiveness.
- Proper Analysis. You must thoroughly examine a measure to conclude the quality of the software to benefit from it.
- Irrelevant Results. Metrics frequently contain meaningless data that distorts the measures. Examples include data problems or test failures brought on by changes to the application.
- Integrity and Acceptability Test Measurement. While statistics on unit tests are easily obtained, properly tracking more sophisticated tests takes work. As it is, a lot of testing teams only have decent visibility into unit tests and not so much into other kinds of tests.
5 Test Automation Metrics: Advantages and Disadvantages
Total test duration
The amount of time needed to execute the automated tests is measured by the total test duration.
- Advantages: Since tests frequently act as a bottleneck in the agile development cycle, test time is an important indicator. Teams will not perform tests at all if they are slow due to the frequent software iterations.
- Disadvantages: Total test duration is not a reliable indicator of software quality because it does not provide information about the calibre of tests conducted.
Unit test coverage
The amount of software code that is covered by unit tests is measured by unit test coverage.
- Advantages: A software codebase’s level of testing can be roughly estimated using the unit test coverage statistic.
- Disadvantages: A unit test is all that a unit test is. Even if every component in an automobile is in excellent working order, the vehicle may not start. Unit test coverage does not account for integration and acceptance tests, which are essential in software as well to guarantee functionality. Additionally, in the majority of programming languages, unit tests only evaluate code that is loaded into memory. The 100% may not represent the code base because often a significant amount of the code is not put into memory and is not examined.
Path Coverage
A measurement of the paths of linear independence that the tests traverse is called the path coverage metric.
- Advantages: Extensive testing is necessary for path coverage, which raises the calibre of the testing procedure. The programme runs each statement with full path coverage at least once.
- Disadvantages: When there are more branches, there are exponentially more pathways. Therefore, the number of alternative pathways in a function with 11 statements increases from 2048 to 4096 when one more if statement is added.
Requirements coverage/test cases by requirement
The features that are tested and the number of tests that correspond with a requirement or user narrative are displayed in the requirements coverage.
- Advantages: Since it tracks how many features supplied to consumers are automated, this is a crucial indicator of the maturity of automated test reporting.
- Disadvantages: Requirements coverage is an ambiguous metric that is challenging to define and monitor over time. A requirement-related test may only validate a subset of the functionality and offer very little benefit.
Percentage of Tests Passed or Failed
This measure just counts the proportion of recently successful or failed tests to the total number of tests that are scheduled to run.
- Advantages: Counting the tests that pass or fail provides a summary of the testing process. It is possible to make a bar graph that displays test cases that pass, tests that fail, and tests that are still pending execution. You can compare numbers from several releases on various days.
- Disadvantages: Merely counting the number of test cases that pass indicates nothing about the calibre of those tests. For instance, even though the software is not operating as intended, a test may pass because it verifies a simple condition or because there is a mistake in the test code. Furthermore, the fraction of the programme that is truly covered by tests is not disclosed by this metric.
Conclusion
For well-informed decision-making, test automation reporting must incorporate critical data including test coverage, pass/fail rates, test stability, execution duration, issue detection efficiency, and ROI.
These indicators help to make the software development lifecycle more dependable and efficient by highlighting the success of QA initiatives, allocating resources, and prioritising issue repairs.
Processes will be streamlined and software product quality will be improved overall by including these metrics in the reporting approach.
