Turn your manual testers into automation experts! Request a DemoStart testRigor Free

Software Test Analytics: How to Design Analytics into Your QA Process

If you’re looking to elevate the software testing process across your organization, one of the most important places to focus on is software test analytics. At testRigor, we’ve found that top-performing teams tend to have robust test analytics in place to measure their metrics at various points to ensure performance is of high quality and consistency across their projects.

If you need some direction for implementing or improving your test analytics, this brief overview will help get you pointed in the right place. Remember, if your team needs direct help with improving your software testing and quality assurance practices, our team at testRigor is always happy to hear from you directly.

A Quick Definition of

Software Test Analytics

First, it’s useful to establish what we mean when we’re talking about software test analytics. Test analytics helps a software team measure test trends in terms of performance and quality, uncovering test issues, performance issues, growing inefficiencies, and so forth.

Implementing test analytics as a regular part of your testing process also means establishing a collection of historical data, which you can use to compare current test data. This can help you identify trends and areas which may need improvement.

By establishing this historical record, you can also measure a pass/fail ratio, error rate, and other metrics to help you better understand performance.

Why Software Test Analytics is Vital for Your Project

Making sure your software project has a robust process for test analytics is of critical importance to any high-performing software team.

Not only does test analytics help you establish metrics and identify trends in test performance, but it also helps you communicate the results to other areas of the organization through hard data and the ability to visualize that data. While software testing isn’t the specialty of most people in most organizations, test analytics can help make understanding software testing far more accessible and can support your team to rally efforts around essential goals.

Furthermore, implementing and analyzing test analytics in your software project is also a meaningful way to improve quality for your end-users and customers. Failing tests or downward trends in performance can mean issues are going undiscovered and unaddressed. Slow test suite execution time can result in slower builds and slower time to market, which means a slower pace of delivering value to customers.

Test analytics can also provide a massive boon for your organization internally, identifying areas of waste and cost which can be eliminated to keep your organization operating as optimally as possible. Using test analytics to identify inefficiencies and time spent correcting issues with tests can also help highlight poor test design, insufficient coverage, coverage in the wrong areas, and other areas where the testing process can be improved. Improving these areas can also have downstream effects, as teams like support and product will spend less time chasing down and documenting issues and creating plans to address them.

There are a number of metrics tracked through test analytics that directly impact your organization and the ability of your organization to deliver value to customers. For these reasons, a number of our customers at testRigor come to us specifically for help with implementing test analytics to gain deeper insights into their process and find areas to optimize. By pairing our powerful AI features with test analytics, our customers gain insights that would otherwise be impossible to detect, yielding significant findings and benefits.

Since so much of test analytics is about identifying trends and looking at data over time, it’s also crucial to implement a strong test analytics process as early as possible to enjoy the cumulative benefits as near-term as possible. This will also help prepare your organization to scale more effectively and avoid costly inefficiencies as you grow.

Finally, test metrics are becoming easier and easier to collect when you have a rigorous testing process in place. For example, testing frameworks like Jest now have the ability to produce reports with just a single command, making it easier than ever to collect and store historical data or see on-demand reports at a glance. With the effort to get started being so low these days, it’s more important than ever not to overlook collecting test metrics within your organization.

What Metrics are Best to Use for Software Test Analytics

As we mentioned earlier, software test analytics can include a wide range of metrics that can be established across your historical and current data. To help you understand some of the basic metrics that most teams track, below is a quick overview of metrics your team can consider tracking if you haven’t started already.

Automated vs. manual testing – for teams that have automated testing already established in their testing process, there is often still a need for some level of manual testing, whether it’s formally documented or not. By committing to collecting comprehensive test analytics, your team can do a better job of tracking time and effort related to both automated and manual testing in terms of time, cost, and the number of tests. This can help your organization dive in at a detailed level and understand where efforts are being placed and if any adjustments need to be made.

Count of issues and issue types – while this metric is fairly classic and most teams are tracking it, it’s essential to establish a count of issues and issue types as a baseline metric to collect and study. By tracking the issue count and type over time, your team can observe whether there are any alarming trends in terms of short-term surges in issues or areas becoming increasingly problematic. Again, this can help your organization see if resources need to be applied or redirected based on areas of actual need, rather than a blind approach to trying to solve all issues equally across time.

Executed and failing tests – If monitoring the health of all of your tests when they run is a basic requirement for any software team, shouldn’t storing those results and examining them over time also be an expectation for any team serious about quality? By tracking executed and failing tests over time, you can detect if there are cases where not enough attention is being spent on the health of the test suite, and if there are failing tests being ignored for too long. By checking executed and failing tests, and how long failing tests go unaddressed, you can help your organization address problems in the process before they grow into issues that are larger in the scope of what they affect and harder to manage.

Test coverage – You don’t need 100% coverage of every nook and cranny of your software projects, but you need strong coverage where it matters most. These days, it’s easier than ever to produce test coverage reports, and focusing your reports on the most critical areas of your software projects can help you ensure coverage doesn’t slip.

Applying AI to Software Test Analytics

When it comes to the cutting edge of software test analytics, the future is here with AI-powered tools.

As your test data set becomes larger and larger, analysis of testing data can become more and more costly in terms of time and effort for anyone attempting to perform analysis manually. This is where AI can step in and run in the background constantly, automatically alerting you with essential insights while turning down the volume on less urgent or inconsequential information.

Additionally, AI can help you detect patterns of failure in your software projects, alerting your team to issues in the software that you can address more quickly to minimize the negative impact on customers. AI can even help you identify which patterns are most important to address first, so that your team can prioritize the most crucial test cases rather than treating them all with equal importance.

Finally, AI can help you use the testing analytics data that you’re collecting to automatically write and maintain tests via AI, cutting down on the manual effort of your team over time and producing huge advantages in scalability moving forward. This is one of the key specialties of our AI-powered solution at testRigor, and a reason why more and more organizations are turning to us to solve their testing needs in a smarter, AI-powered manner. If you want to learn more about how testRigor can improve your test analytics process automatically and perpetually with AI-driven automation, don’t be shy – our team is happy to hear from you and help out in any way.

Related Articles

The 9 Leading Android Emulators for PCs in 2024

You can find over 3 million apps on the Google Play Store as of 2024, with a selection of Android apps offering different ...

Test Automation Tool For Manual Testers

When skilled testers use their expertise to ensure software quality without using any programming test script, that testing may ...