Software testing is a complex process and establishing the right metrics to measure its success is a challenging task in itself. In addition, the sheer number of available software testing metrics can make it hard for testers to select the most suitable ones. This article will show you 4 software testing metrics you should be using.
Before getting started, you ought to have the following for deriving most of these metrics.
- Employ version control systems like GIT.
- Practice writing clean codes right from the unit level.
- Robust test automation framework that allows integration with test management tools and others defect reporting tools etc.
- Set up a base for CI/CD pipeline for building code.
- Ensure test management traceability.
- Run tests in an automated fashion after each successful build.
Now let’s dive into the metrics!
1. Test Coverage
As your product evolves, new features come into play. And with each successive releases, you must ensure that these new features don’t carry bugs with them and also, they don’t break the functionality of existing features (regression testing)
The amount of time required to test these, and the regression suite is enormous, that we prefer automated testing to achieve higher test coverage and quality standards.
The greater the test coverage, the lesser the possibility of unidentified defects. So, when establishing a % target for your coverage (Companies with mature QA practices attain >95& test coverage), ensure that you do not restrict it to only Unit & Functional tests but also other tests I.e, UI/UX, Performance, Security, so that they cover most facets of your product.
2. Qualified Builds
Some of the builds are passed to QA without meeting specific requirements and get rejected for reasons such as: Not having met the entrance criteria, untestable builds, etc. This means a lot of time is being wasted back and forth between rejecting builds and testing new ones.
Therefore, qualifying every build is crucial to the quality of the software. Each stage must have stringent quality gates, and one way to making this process seamless is “automated builds.” It helps automate tasks right from collating source code to integrating deployments at various settings. Typically, in an agile world where companies are fast embracing the continuous integration/continuous delivery (CI/CD), promoting builds in an automated fashion makes testing efficient and contributes greatly towards a stable release.
Companies with mature QA practices aim for 100% automation of builds.
3. Regression Targets
As discussed earlier in this blog, regression plays a crucial role in maintaining product stability & maturity. You should set a target for automated regression tests and their execution frequency to achieve higher test coverage. Typically, a regression suite will have test cases that:
- Have the greatest number of defect rates.
- Undergo frequent changes.
- Pass/fail previously.
- Covers the core features of the product.
- Have features that are more obvious to the users.
- Include boundary values.
Depending on the resource constraints, QA will re-test every case or select few (that are most likely to be affected by the recent changes) or prioritize test cases (depending on their impact on the business, failure rate, frequency of usage, cost to fix them)
In most cases, manual regression testing will not cut through, and test automation is the only way to maintain quality without sacrificing much of the costs.
Companies with mature QA practices have at least 85% of their regression suite automated, i.e., tests are executed automatically after every commit in the CI/CD pipeline.
4. Defect trends
The very essence of software quality means releasing a defect-less product. However, product owners and quality engineers walk a tightrope when there’s a release, as any critical defects reported by the users’ post-release may cost huge for the company both in monetary terms and reputation-wise.
So, a close watch at the various defect trends like defect detection, defect removal, escaped defects at different stages will help provide critical insights into improving many other metrics.
Companies with mature QA practices have these metrics established that make the tracking of defects much more manageable (defects to test case mapping), and it also helps in the early identification of critical defects.