Essential QA Metrics to Measure Product Quality
An effective QA strategy always can be measured. It should help measure product quality, team efficiency and must provide critical insights into your software testing process.
In this post we will understand more about how QA metrics can help in measuring product quality and must bring in usage by every quality leader.
The Importance of Measuring QA
QA processes can be full of friction that slows down deployment and eats up valuable resources. Tracking key metrics around the QA process helps identify where your team’s time and budget are being used ineffectively and optimize accordingly.
Equally important is measuring the efficacy of the testing process — bugs that slip through to production are expensive to fix and can negatively impact customer confidence in your product.
Hence its very much important both for Manual and Automation tests to measure QA in right manner.
Finding the Right QA Metrics for Your Team
Because development and QA processes vary greatly from team to team, the measurements that matter can be different depending on the team makeup, tools and software used, customer expectations and more.
As a baseline, the quality metrics your team tracks should be:
3. Track able over time
4. Maintained and updated regularly
5. Tied to business goals
With these parameters in mind, there are a few key numbers that every team should consider including in their QA metrics.
1. Test Coverage
This metric determines the number and spread of tests across the code base. This provides insight into where your resources are being used.
It helps you understand which areas of application under tested and also determine where your test team should spend more efforts.
2. Flaky Tests
Broken and unreliable tests that are not providing useful quality feedback termed Flaky tests. Flaky tests not only waste time and resources, but also reduce overall confidence.
Tests that Pass or fail intermittently fall under flaky tests and must be fixed as early as possible.
3. Time to Test
Amount of time it takes to run and report results for a set of tests. This helps teams understand how to make testing cycles as efficient as possible.