Digital confidence, the feeling that everyone in your organization expects your application to work exactly as it was designed, is a journey that requires a number of strategic and tactical considerations. From cultural buy-in from the top to tooling and resources that help support continuous testing at scale, there are a number of things that can help build confidence that your applications are providing flawless user experiences. However, one of the most important things to consider when scaling your testing efforts across your pipeline is what to do with all of that data!
With an increase in testing frequency and volume comes larger sets of test data. This is why data analytics tools have become a critical component in any journey to digital confidence. Teams that get this right not only have insights into where quality risks might still exist, but they can also use data as a roadmap to help continuously improve their testing practice and processes.
But how can you accomplish this? In this blog post, I will outline three different ways that data analytics can help further accelerate release cycles and keep teams productive as they strive to provide the best digital experience to your users with every release.
The most common benefit of big data is the ability to view aggregations that can help uncover trends across teams, lines of business, or even the organization as a whole. For testing, this means that you can use data insights to understand where your practices are working best, while identifying areas that might need improvements. Some key metrics to consider include:
Browser, OS and device coverage - does your quality strategy ensure your applications work for every user? Data insights can uncover where there might be gaps that create risk.
Pass/fail rates - a macro view of data can show a historical long view of test quality. Identifying teams with consistently failing tests can help sharpen focus into where you need to implement better practices.
Resource usage - seeing how your resources are used across different teams can help you understand if you are getting the best return out of your investment into certain tools, and can also identify potential bottlenecks in resource allocation.
Along with the macro view, data insights can also be used with individual tests in aggregate to understand how they are performing over time. Analytics tools are often the first alarm bell to sound when things aren’t going right. Using a similar lens as you would at the organizational level, your data can give you further insights into:
Individual test performance across different browsers, OSs and devices
Run times of individual tests to help you understand where bottlenecks might occur
Error versus failure rates that allow you to understand which tests need further optimization
So far we have been talking about traditional analytics reporting, which is simply customizable data in aggregate. As discussed, there are a number of ways in which these static reporting tools can help you find areas in your testing practice that need extra attention. But with the advent of machine learning and more sophisticated analytics capabilities, the future isn’t in the ability to present the data, but instead to make it actionable.
One example of this is failure analysis, or the process of analyzing a failed test to figure out what went wrong. Traditionally, data analytics tools present only aggregate info. For example, they will show you how often a test is failing over time. Using machine learning techniques, now you can have that insight, plus actionable information into where in the test that failure occurred. And finally, that same machine learning understands the failure reason, searches across the rest of your test suite, and identifies all failed tests that contain that same reason. For developers and QA engineers responsible for fixing failing tests, this kind of technology provides the perfect roadmap. They can understand what the most pervasive issues are, and where they are occurring, giving them more “bang for their buck” when they are tasked with improvements. The gains in productivity, release acceleration, and overall trust in automation are significant when you can use your data in this way.
Teams that want to achieve digital confidence know that it’s important to tame the data beast. By either building their own tools in house, or using a third-party vendor, those organizations that have a deeper level of visibility into the efficiency of their testing efforts can better understand risk, prioritize improvements, and ultimately realize the true value of continuous testing. And for those companies that operate in highly competitive markets, it can often be the secret weapon to digital success.
Sauce Labs offers rich data reporting and analytics on our newly updated Insights platform. With various reports in an intuitive and customizable interface, and sophisticated machine learning under the hood to help with failure analysis, Insights gives teams the actionable data they need to test better, and ensure that they are delivering the best digital experiences to customers. To try it for yourself, sign up for a free trial today.