Almost every QA and software testing company will attest to the fact that maintaining peak productivity is the only way to ensure the reliability of your testing workflow and in the long run, the reputation of your business. To cement an ethos of productivity, QA performance metrics are used. Besides the obvious, these metrics also enable managers to efficiently track the progress of the QA team over time and make key data-driven decisions on any future projects.
The QA performance metrics you choose should be well-placed to accurately identify if all the goals you’ve set are being met and all the resources are being utilized. The implementation of these metrics should also typically extend beyond the executive managerial level to the entire QA team, thus improving productivity across every channel.
In a recent interview with Kualitatem’s Service Delivery Manager, Ishfaq Zia, a QA stalwart, few key points were highlighted that brought valuable insight into how he runs and operates his QA team using performance metrics.
“It’s important to get your clients on board with your process from the start. Once the expectations are understood, our approach is to prepare a high-level demo and then highlight how we solve their problem,” he said. “We usually are on a time crunch and there’s a lot of work involved, especially if it is something the team isn’t familiar with. Having metrics in place, the team completes the breakdown of their long term goals into weekly goals which then translates into meeting timelines.”
The Need For QA Performance Metrics
Boasting impressive numbers to tout your team’s performance is something most of us are guilty of doing. What’s important to understand is that while these numbers look great on paper, they don’t help much in generating real results, i.e. revenue or team productivity.
This is one of the biggest reasons why successful QA testing companies focus solely on metrics. These metrics enable the teams to look deeper into the operations and locate any inefficiencies that might exist. QA metrics solve any performance and testing challenges from the get-go by utilising tangible data to move to a calculated solution.
QA Performance Metrics To Measure
While the benefits of utilizing QA performance metrics are clear, the results are only truly realised if your team reviews the right numbers.
Let’s look at a few QA metric examples that your team can start implementing today:
- The number of defects located in any build
Ideally, moving from one build to the next should reduce the number of bugs found over the duration of the project by your defect tracking tool. But this isn’t always the case and can especially be polarising if a new feature is added in a build. QA metrics need to be set in place to track the stability of the builds and compare them amongst each other as the project progresses. If your team reports an increase in this metric, it may be due to either or all of these issues:
- While regression testing the same defect, multiple issues are located using one defect and reported as new issues.
- Spot checks are not conducted before a test environment is delivered to your QA team.
- Communication lapses are occurring between your QA services partner and on-site team.
- The number of defects located in any build
- The amount of time taken when executing a test cycle
Testing times should ideally fall as your QA team familiarises themselves with each test case and ensures smoother subsequent runs. To measure this metric for efficiency, note how much time it takes to run selected tests. You may also make it more useful for your QA team by noting which tests can be run simultaneously or concurrently to save time. If your team reports an increase in this metric, it may be due to either or all of these issues:
- An outpour of information in defect tracking tools relating to issues and bugs
- QA team is not familiar with test cases or product
- Communication lapses in onsite and offsite teams
- Testing requirements are not rigorously set and are subject to change
- Software/hardware configuration issues
- The number of executed test cases
If the goal is to provide value whilst maintaining efficiency, monitoring the percentage of automated test cases is vital. Doing so can help your QA team have a clear path of action for any unresolved test cases in a module. If your team reports a decrease in automated test case numbers, it may be due to either or all of these issues:
- Your module’s testing system is unstable
- The affected module is out of date
- New automation scripts are being generated which stops testing if the affected module receives frequent changes.
- The severity of bugs located during the production stage
The ideal scenario for any testing timeline is that once deployed, the software or application has no bugs.
“Exhaustive or complete testing is impossible. Occasionally, a client comes around after several rounds of our own testing and locates a major bug that we should’ve picked on. While there is always a possibility, ensuring a complete understanding of the product and utilising proper tools is essential to negate this risk as much as possible,” is what Ishfaq had to say to reduce the number of bugs before production.
Your team can also alternatively measure how many bugs – depending on their severity – makes it into production during deployment. If your team reports an increase in this metric, it may be due to either or all of these issues:
- Thorough regression testing is not performed
- Not enough test cases are being automated
- A post-deployment strategy is not designed by your QA team
How To Present Performance KPIs
Measuring the right QA performance metrics is just as important as how you present this information to your internal audience, be it c-suite executives or IT peers. These QA metrics examples can significantly speed up your product quality and QA team’s productivity, so setting up your QA analysis process to ensure that your team can receive the most benefits from your data is vital.
When presenting performance KPIs, make sure to:
- Research and invest in the best tools that’ll help you measure and track your performance numbers.
- Get rid of all vanity metrics from your performance report.
- Any reasons for subpar performance and strategies should be looked at and reported using data-driven insights.
By applying the aforementioned QA metrics, you can enable your QA teams to work far more effectively and track their progress over time. Measuring the proper quality assurance performance metrics can quickly return impactful, measurable results upon implementation. Don’t hesitate to incorporate additional metrics that measure performance efficiency in order that you still maximize the productivity and deliverability of your QA team.