AI has taken over the world in the last year — though thankfully not in a Skynet kind of way. More recently, we’ve seen it implemented in anything from chatbots to art and software development tools. One of the most significant impacts we’ve seen is within software testing.
The benefits of AI in software testing are numerous, with tools dramatically improving the overall testing lifecycle and the overall developer experience. In this article, we’ll discuss some of the biggest impacts of Artificial Intelligence methods in software testing and why you should consider them for your engineering organization.
Breaking Down the Biggest Benefits of AI in Software Testing
When it comes to testing software, AI has already been around for a while but is now beginning to truly hit its stride. Here are just a few of the benefits that come along with incorporating AI and software testing.
Increased Test Suite Efficiency: With some AI tools generating tests on their own, whether by analyzing human input, or figuring it out on its own, developers have more time to work on unique or complex tests. With methods like Predictive Test Selection, tests can be selected and run as needed instead of constantly running unnecessary tests.
Reduced Resource Costs: Thanks to these faster, more efficient testing cycles, QA teams can reduce overall costs, especially in the testing lifecycle. These tools alleviate the need for extended time to write and run tests, with AI tools running with minimal human interaction required.
Improved Testing Accuracy: Flaky tests can be almost entirely eliminated with the power of AI. Tests can be created with more resilience and can be continuously improved to prevent extended delays in your testing lifecycle. And with Test Impact Analysis, your team will be armed with the knowledge to make their tests even more reliable.
Testing and Feedback Velocity: Naturally, these tools will help your teams in many ways. And with all of these combined, your team’s output will dramatically increase. Teams with fast and accurate tests can push out updates and features faster and more frequently, giving your product a competitive edge.
AI-Powered Test Automation
So how does AI actually transform and benefit your testing lifecycle? Using Artificial Intelligence in software testing can be done in a variety of ways, but here are three of the most common AI-powered test automation approaches.
Automated Test Scripts
Your teams can leverage the power of AI to create your test scripts, saving your team resources. AI tools are able to develop complete tests without writing a single line of code. Many tools opt for a no-code approach to writing tests, enabling anyone on your QA team to get their hands dirty writing loose instructions, which the AI can interpret into actual tests to run. Or, an AI tool can watch you run your tests manually by clicking and testing your application, thus training the AI to act like an end user for better coverage.
Automated Test Data Generation
In the past, it was fairly common for QA teams to create their own test data, as using production data usually comes with too many risks. However, with AI/ML tools at their disposal, they can create synthetic data that looks and acts just like production data without any liabilities. Plus, these tools can create dynamic data on the fly for teams to test with specific data structures as needed.
Now that tests have been created and test data is readily available, teams can use AI to manage the testing process completely. AI tools can select and run tests through automation and use neural networks to pick and choose the right tests to be run. After tests are run, some AI testing tools are able to analyze and self-heal their tests on the spot. And with methods like Launchable’s Predictive Test Selection, teams can get feedback from their tests faster, leading to a faster testing cycle overall.
AI-Powered Testing Analysis
Of course, once testing is done, that doesn’t mean the whole lifecycle is complete. With AI testing tools at your disposal, your teams can have more intelligent bug detection. Facebook’s own Getafix tool helps their dev team find and send fixes to developers to approve.
When something goes wrong, it usually takes teams time and effort to find out what happened and what caused it in the first place. Now, there are AI tools that can help your team investigate these errors with something called “automated test detection.” These tools study your application, detect anomalies as they occur, and then hunt down the root cause.
Once all your tests are complete, you’ll want to know how they all performed. And in this data-driven world, you’ll need the hard data to show performance. Many AI tools can assist your teams in visualizing the coverage of your test suites and can offer recommendations for new tests to be created.
Terminating Long Test Cycles with Launchable’s AI
Launchable understands it’s not simple getting all of the benefits of AI in software testing from a single source. But that’s why we developed an AI-powered dev intelligence platform that elevates test selection for faster test runs without sacrificing quality.
With Predictive Test Selection, our ML model analyzes the data from your existing tests and code and can help you make data-driven decisions to improve your testing process. The Launchable platform intelligently runs the most impactful tests based on the changes made to the code to help reduce the overall testing lifecycle.
Our platform can also handle your entire test suite, giving you deeper insight into how your tests perform, showing you what tests have the greatest (or least) impact, and giving you meaningful metrics to monitor your tests run.
With Launchable being language and test framework agnostic, it’s easy to integrate into your software development cycle. Whether you are looking to reduce testing cycle time in-place or shift left, you can start using Launchable in four easy steps. Test earlier and more often for a better product overall, with Launchable.