Artificial Intelligence Methods in Software Testing

AI methods upping the ante in software testing innovation 

Key Takeaways

  • Whether scaring Google and college professors with ChatGPT or an Endless Seinfeld Episode, Artificial intelligence is making huge headline waves - but Artificial Intelligence Methods in software testing has been expanding over the last few years.

  • Through the adoption of AI within software testing practices and tools, teams can test faster and more confidently, while also helping teams write better tests overall.

  • Four AI method categories for software testing include declarative, differential, visual, and self-healing, all of which we share leading software testing platforms for each category.

Whether scaring Google and college professors with ChatGPT or an Endless Seinfeld Episode, Artificial intelligence is making huge headline waves. AI has already been relatively prevalent in the tech world for a few years now, but as the power of AI grows, so too does what we can do with it.

One of the more significant impacts we’ve seen over the last few years is Artificial Intelligence methods in software testing. Through the adoption of AI within software testing practices and tools, teams can test faster and more confidently, while also helping teams write better tests overall.

Artificial Intelligence’s Place in Software Testing

With the increased efforts for software engineering teams to make data-driven decisions, the amount of data available to teams has grown exponentially. With this abundance of data, testing teams can easily use it to improve their overall testing life cycle. We all know that testing is a long, resource-intensive process. But with intelligent automation (thanks to the onset of AI/ML adoption), teams can save on costs while dramatically improving their testing process.

These changes are already here, too. AI has been implemented in popular testing tools that empower devs to create test suites automatically or analyze test results better for a more informed look at your product. Your teams can even use methods like Predictive Test Selection to run the proper tests intelligently and create shorter, more meaningful tests. And with the insights available from these tools, teams can revise their overall testing cycle to improve efficiency and reduce costs.

Artificial Intelligence Methods are Changing Software Testing for the Better

We already covered a few of the benefits of AI in software testing, but let’s break it down a little deeper. The implementation of artificial intelligence in your testing cycle can impact it in several ways, such as:

  • Speed - AI tools can create and run tests by themselves with machine learning and neural networks. Plus, these tools can self-heal their tests, reducing the need for human intervention.

  • Quality - AI doesn’t just run your team’s tests, but it can improve them too. AI testing tools can analyze test results and shape them to improve coverage. Additionally, AI tools can utilize machine learning to emulate users, resulting in more accurate tests.

  • Innovation - These tools go beyond just your standard tests. They can detect the coverage of your test suites and help your devs fill in the gaps. They can also use visual inspection to assist with UI/UX testing.

  • Developer Experience - Finally, AI tools can help improve your team’s overall developer experience by cutting down on tedious work that your devs perform, such as analyzing which tests passed and only running the tests that need to be.

Four Artificial Intelligence Methods Growing in Software Testing

Now that we’ve seen how AI can improve your testing processes on a grander scale, let’s get personal with it. Here are four next-gen Artificial Intelligence methods in software testing and the top testing tools within each category and how they’re already impacting testing.

  1. Self-healing AI testing tools like Testim: One prominent Artificial Intelligence method in software testing is self-healing AI, designed to help address the persistent issues of flakiness, reliability, and maintenance. To combat these issues, self-healing tools have been created that are largely based on a record and playback system. This system has a primary ML engine that is responsible for the self-healing of the recorded scripts. Testim boasts fast and flexible AI-powered authoring, execution, and self-maintenance of end-to-end tests, built for scale. Testim’s AI-powered stability works as a self-healing mechanism that automatically evaluates confidence scores from previous test runs and as elements change, improves for more stable, low-maintenance testing.

  2. Visual AI testing tools like Applitools: Due to the continual rise in the number of platforms with varying screen sizes and configurations, testing the UI layer has become a laborious task for test engineers. With orgs regularly making alterations to the UI layer to provide better user experience, visual AI testing methods can help check all versions of these UI layers. Applitools is a test automation platform powered by Visual AI, designed to increase test coverage and reduce maintenance with cross-browser and cross-device testing. This platform integrates with other test frameworks and is compatible with many existing testing tools like Selenium, Appium, and Cypress.

  3. Declarative AI testing tools like Tricentis: This AI method is designed to reduce mundane repetitive tasks through intelligent, dependable automation. Within this category of software testing enhancements includes Model-based Test Automation (MBTA), Robotic Process Automation (RPA), Natural Language Processing (NLP) and Autonomous Testing Methods (AT). Trecentis is a great example of the Declarative AI method Tricentis enables Agile and DevOps teams to quickly achieve their test automation objectives. Test APIs and GUIs with Tricentis’ end-to-end software testing, that combines test data and case design and generation with test automation.

  4. Differential AI testing tools like Launchable: Most testing cycles take a long time to finish, with tests being run constantly to ensure your team is confident in their results. However, this usually means that teams spend a ton of time and resources to get that confidence. Differential AI testing tools use AI and ML algorithms to identify code-related issues, security vulnerabilities, and regressions. With Launchable’ AI-driven platform, engineering teams can test five times faster. Reading your git metadata to analyze your existing tests, Launchable’s Predictive Test Selection subsets your tests to prioritize the most impactful ones. That way, your teams can test less and ship more. Teams cut down on infrastructure costs by running the right tests when it makes the most sense. Your complete test suite doesn’t need to be run for every change, so Predictive Test Selection helps run the tests that matter most, saving time overall. 

Final Thoughts on the Future of Software Testing and AI

Artificial intelligence algorithms can emulate human intelligence, and machine learning allows computers to learn automatically. Artificial intelligence and machine learning combine the development of algorithms that can access data, analyze it, extract patterns, make predictions, and use those predictions for software testing.

Organizations are rushing to acquire tools that can leverage AI and machine learning algorithms to test software efficiently. Aside from enabling faster and continuous quality assurance, Artificial Intelligence methods in software testing have also been shown to enable intelligent automation without human intervention, resulting in a quicker return on investment for businesses. Launchable helps teams to launch fearlessly by using AI and the data from your code and tests, to model data-driven decisions that accelerate your development. Explore the intelligence behind Launchable today.

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