The 13 Most Best Software Testing Metrics for Data-Driven Pipelines
Software testing metrics you should be measuring for useful test insights.
Key Takeaways
As with all software testing metrics, just because you can measure it, doesn’t mean that it adds value overall.
We rounded up the 13 most common valuable software testing metrics and explains when and how to use them.
Software testing is ripe with a tsunami of data outputs, which makes it a perfect match with machine learning for new software test suite insights.
Explore 4 new test suite insights including Flaky Tests, Test Sessions Test Session Duration, Test Session Frequency, and Test Session Failure Ratio for a ML-driven pipeline.
Data runs the world today with about 2.5 quintillion bytes generated daily. From this data, we can measure and improve how we operate in our daily lives and bring massive improvements to our work. But how do we know what data is important to keep an eye on, especially when we’re assessing software testing data?
One of the ways we can do this is by using software testing metrics — measurements of different aspects of your development and testing lifecycle — to shine a light on how teams are performing. By using these metrics, teams can paint a picture of their performance and spot roadblocks in their workflows that can be detrimental in the long run.
The Data Roundup: Common Software Testing Metrics
There’s a famous idiom “You can’t manage what you can’t measure” that’s often attributed to W. Edwards Deming, a statistician and quality-control expert, or Peter Drucker, one of the world’s most famous management consultants.
But you can’t run on visible figures alone - just because you can measure it, doesn’t mean that it adds value overall.
Measuring the right data means you can make the right data-driven decisions for your software development and testing. We’ve rounded up software testing metrics that allow teams to analyze their test outputs for higher quality outputs and faster testing pipelines.
Change Failure Rate: Accidents happen, and so does downtime — it’s unavoidable. Measuring your team’s change failure rate means measuring how many deployments led to a failure in production. By tracking this, teams can take a step back and investigate not just why a deployment may have failed but to see why they might have made a mistake in pushing to production in the first place.
Customer Satisfaction Score: Naturally, software is built for one purpose: its users. So if your users aren’t happy, it puts a damper on your team’s mood. There’s no finer testing suite than throwing it to your user base to break and tinker with every corner of your software. However, this isn’t a solid metric to rely solely on, as it doesn’t paint an accurate picture of your users or the effort your teams put in to make that killer software.
Cycle Time / Lead Time: These metrics are relatively similar and stem from the Lean philosophy of car manufacturers. Cycle time tracks the time from the first commit of a project to the completion of the project, giving you an idea of how long a team takes. On the other hand, lead time is a measurement from when a project is started to when it’s completed and in the hands of the customer.
Defect Detection Rate: Sometimes known as “defect detection percentage,” this is a measurement of the quality of your testing process. It measures how effective your tests are by dividing the number of defects found in a phase of your testing cycle by the number of total defects in that phase, then multiplying by 100. This helps spot how effective your tests may be, as a significant number of defects found after the fact can signal weak or inefficient tests.
Deployment Frequency: Deployment Frequency measures how frequently a team pushes to production and shows how fast a team can be. The simplest way to measure it is by simply counting how many times a team makes deployments in a specific time frame, so it’s pretty easy to track. Some teams aim for weekly or bi-weekly deployments, while others aim for daily deployments, so try to find a timing window that works best for you and your team.
Mean Time to Failure: Tracking how fast it takes to get up and running is one thing, but you should also be tracking how often it happens in the first place. Measuring the time between downtimes gives your teams an idea of how often they can expect a potential downtime and how to prepare for them in the future.
Mean Time to Repair: No software can guarantee 100% uptime, no matter how much effort you put into it. By tracking your mean time to repair (MTTR), you can monitor how long it takes teams to fix and test issues, giving you insight into your processes' effectiveness. It’s a simple measurement; all you need to do is measure how long it takes for a team to diagnose, repair, and test a solution. Then, compare it to each other event, and calculate the average.
Team Health: We’re all a bit more health conscious after these last couple of years. This metric doesn’t track your team’s physical health but instead measures how much work your individual team members are doing and their overall developer experience. Nobody wants to do the work of two other people, so by measuring team health, you can distribute work evenly and keep your employees happy and healthy.
Test Case Coverage: As the name suggests, test case coverage measures how much of your code base is covered by tests. When teams plan out testing, they should list the requirements they need to meet and how to cover them. If teams are testing a lot but not testing the entirety of the code base, then your coverage is lacking somewhere.
Test Case Pass Rate: Teams test software to ensure it’s up to snuff before being sent to production. By measuring your test case pass rate, you’ll have a better idea of the quality of the software as it goes through the testing process. To measure this, you’ll need to divide the number of passed tests by the total number of tests run.
Time to Restore Service: Similar to change failure rate, this metric involves the worst: downtime. To measure the time to restore service, you need to calculate how long it takes for your team to recover from a failure in production. This can help shed light on how your team works together in crisis mode and how to improve it in the future.
Velocity: As you’d guess, this metric is all about speed. Specifically, it’s how long an Agile team takes to complete a sprint. However, the glaring downside of this metric is that it doesn’t show how your team was fast. Velocity doesn’t factor in things like complexity, size, bottlenecks, or anything. So you can see how this metric can be used and why it’s ineffective in practice.
Work in Progress: As you’d think, work in progress measures how many active tasks are in progress at once. If a team has too many unfinished tasks, it’s a great indication there are bottlenecks somewhere in your development lifecycle. This allows your team to sit back and assess where these roadblocks are and how to resolve them.
Tracking and Analyzing Software Testing Metrics
Now that you know what you should be tracking, how do you actually track them? There are plenty of ways to track all your metrics, each with its pros and cons. Let’s break them into three major categories:
Manual tracking methods: Pen and paper, excel sheets and formulas, and reporting are all within this realm. This is a great way to get started with your metrics for small teams, but it isn’t sustainable for long.
Automated testing tools: The current standard of the development world. Automation allows dev teams to collect and track all the data for them and usually offers insights and reporting. It enables devs to take a deeper look into their development lifecycle and spot bottlenecks in their processes in much finer detail than manual tracking.
Machine learning algorithms: The newest kid on the block, ML algorithms combine the heavy lifting of automation with the power of logic to help teams track metrics. Not only does it help track, but it also helps spot bottlenecks for you and can show you the metrics that matter most.
Using Software Testing Metrics to Improve Your Testing Process
Information is the key to success, meaning you need to collect and use the data available to you to make the most impact. Using these metrics to spot areas lacking, you can start to formulate a plan to improve them.
With a proper plan in place that’s backed by the metrics you’ve gathered, you can start to prioritize your team’s testing efforts. Areas you may have been unaware were lacking will quickly come to light, allowing teams to elevate your testing process as a whole. And over time, you’ll be able to make informed, data-driven decisions over the quality of your software and the tests behind it.
Challenges and Considerations in Tracking and Analyzing Software Testing Metrics
If information is power, with it comes great responsibility. You’ll need to ensure that the information you use is accurate, and you should only use the metrics that apply most to your team’s needs. If you try to solve every problem at once, or your data isn’t correct, you’ll waste precious resources trying to fix it.
Tracking these metrics is essential, but you can’t value all of them equally. For example, velocity is a popular metric for Agile teams, but it can quickly degrade the quality of your software and your team’s health if you try to move faster than your team can keep up. Balancing the costs and benefits from analyzing and improving these software testing metrics is vital to the overall developer experience of your organization.
4 New Software Testing Metrics from Machine Learning Insights that You Need
It’s essential to understand the pain points that can come along with the testing cycle, and that’s where machine learning shines. Some of the software testing metrics we discussed can show you how your teams are performing at a glance, but they can’t paint the full picture of why they arose in the first place.
Software testing tools with machine learning models can give you insights into not just where these bottlenecks are happening, but they can also learn from your data outputs overtime and offer insights into why they’re happening.
Launchable’s Test Suite Insights monitors the health of test suites to flag issues and fix them before they impact your developer experience and product quality. Tune out the noise of your testing suite and navigate the chaos that comes with these large suites. Launchable helps your measure metrics that directly improve your SDLC:
Flaky tests get prioritized by how impactful they are. Test sessions are analyzed daily, and our Flakiness report shows you how flaky your tests are, so you can fix and run them more reliably.
Test Session Duration is tracked every time you run your test suite, showing you how long they take across multiple sessions. Tests that take too long will be highlighted here, so you can spot what went wrong and try to fix it.
Test Session Frequency shows you how often your test suites are run and aggregates it with session duration. That way, you can ensure tests are being run often. Plus, our Predictive Test Selection can ensure the right tests are run at the correct times, saving time and resources.
Test Session Failure Ratio will highlight tests that are failing more frequently. With that information, you can investigate whether there is an issue with the test itself or signaling a deeper issue with the current build.
Test Insights alone can help teams make your testing life cycle more efficient, but they can’t help you explain where roadblocks arise. With Launchable’s Predictive Test Selection, you gain access to a powerful ML model that’s entirely focused on improving your testing. It boosts developer experience by identifying and running tests with higher chances of failure. It also helps speed up your feedback loop by giving you the software testing metrics that matter most.
While there are a lot of ways to analyze and measure the tsunami of data within your pipeline, the most effective software testing metrics are the ones that make your process more reliable, faster, and allows you to deliver your best product. https://www.launchableinc.com/blog/13-best-software-testing-metrics-for-data-driven-pipelines
