What Is AI Testing?

AI testing is the process of using artificial intelligence (AI) in software testing. Traditional software testing approaches combine automation and manual processes to ensure software works as expected in normal and challenging circumstances. AI testing us AI models to solve common testing challenges and speed up testing and test maintenance. It introduces efficiencies by intelligently automating tasks such as test execution, data validation, and error identification.

Developers worldwide use automated software testing frameworks, tools, test scripts, and patterns to verify and validate software while boosting productivity. The testing tools help create unit test stubs, merge-based integration testing, and automate the test environment. In DevOps, these practices are known as continuous testing. It helps speed time-to-release and make test practices repeatable and transferable. You can also catch bugs early, eliminating more errors in production.

However, new AI testing tools and practices are advancing software test automation beyond continuous testing. For example, AI software testing can:

• Suggest code fixes on or before test failures.
• Generate test scripts and test data automatically.
• Develop entire business or project-specific test suites rapidly.
• Self-adjust test suites based on software changes.
• Run visual-based pass/fail test automation.
• Edit tests based on live software interaction.

Beyond these, the future capabilities of AI software testing include benefits like:

• Automatic zero-day exploit vulnerability test automation
• Qualitative UX acceptance tests
• Dynamic adjustments to live software based on AI tests

The benefits of AI testing for enterprise include

Improve testing speed

AI testing identifies patterns and anomalies that might take human testers much longer to spot. This rapid analysis allows for immediate feedback and faster development cycle iteration. Additionally, AI can prioritize test cases based on their impact and likelihood of failure, ensuring that critical issues are addressed promptly.

Increase test coverage

AI automatically generates many test cases, including those that human testers might overlook. It can dynamically create and adapt test scenarios and traverse numerous paths within an application. This allows you to cover edge cases and complex user interactions that are often challenging to simulate manually.

Reduce testing effort

AI testing reduces both manual workload and the likelihood of human error. It automates repetitive and time-consuming tasks like test data generation and setting up testing environments. AI testing frees up human testers to focus on more strategic activities like test planning and exploring innovative testing approaches.

Simplify test maintenance

AI testing automatically adapts test scripts to application changes. If an update modifies the user interface or alters functionality, AI-powered tools can detect these changes and adjust test cases accordingly without human intervention. Test suites remain robust and relevant and provide accurate feedback on the application's current state.

AI testing is most often used in test creation, generating test cases and test scripts for software testing processes across different software testing types. We give some examples below.

Unit testing

Unit testing involves testing individual components or units of a software application to ensure each part functions correctly in isolation. AI automatically understands the expected behavior of the unit and automates the generation of test cases and assertions. It can predict and create tests and refactor them as the code changes. AI testing significantly improves the quality and thoroughness of unit tests.

User acceptance testing

User acceptance tests check that the software meets the requirements outlined in the software requirements specification and behaves as a user would expect it to while running. Large language models can generate user acceptance tests using plain language, as described by the software developer or a software tester. You can also direct AI software bots to interact with the application and run through these user acceptance tests.

Performance testing

Performance testing checks that software runs as expected under unusual conditions, such as high transaction loads or unstable network connections. AI can analyze large volumes of performance data to identify trends. It can use that information to dynamically adjust test parameters in real-time and simulate realistic and challenging scenarios. AI testing tools also forecast how new changes affect an application's performance before deployment. Thus, AI testing optimizes performance proactively rather than reacting to issues post-deployment.

Regression testing

In regression testing, tests are run to verify that new code changes do not make tests fail. AI regression test automation identifies where changes have been made and adjusts tests or highlights areas of concern to the developer.

AI-powered testing tools can be bought as a pre-packaged commercial product or platform or built by developers in-house. Depending on the solution, these are some methods used by AI testing tools.

Self-healing

Many AI test tools use self-healing to adjust test cases based on changes to the underlying code or UI. This eliminates the need to manually adjust tests when code changes and false test failures when the tests have not been updated.

Regression

Regression analysis can be used in software testing to determine the answer to questions like:

• What are the most critical test cases to run?
• What is the percentage of test coverage?
• What is the minimum set of test cases?
• Which test cases are likely to fail?
• How long will it likely take to fix a bug based on a specific test case?
• How will the software perform under the given performance tests?

These regression-based analyses can help determine where to focus work efforts and update timelines and cost estimates in software project management. They also guide test orchestration and test automation.

Defect analysis

Defect analysis is used in AI software testing to deeply examine the causes of test failures and bugs in code. By training on code bases and existing and fixed bugs, AI-based defect analysis can determine the following:

• The parts of the software that often contain bugs
• What types of mistakes are typically occurring
• How and where bugs compound, and the effects
• The root cause of complex bugs

These determinations can help guide strategies for developer training, code refactoring hot spots, and complex test coverage. By learning more about how, where, and why defects occur, they can be more easily avoided in the future.

Visual testing

AI testing uses image recognition and computer vision to identify where mistakes and bugs lie in UIs based on previous UIs and other product UIs. Traditionally, this process involves humans navigating through the software, which takes time and can miss errors. Using artificial intelligence testing instead requires less human effort for fewer run-throughs.

AWS CodeBuild is a fully managed continuous integration service that compiles source code, runs test automation, and produces ready-to-deploy software packages. CodeBuild integrates with AWS CodePipeline to control the full DevOps pipeline for continuous delivery through application and infrastructure releases. You can add and manage your AI test tools and run fully automated testing within these services.

Amazon Q Developer is a generative AI-powered software development assistant. Based on your comments and existing code, it generates real-time code suggestions ranging from snippets to full functions. It also supports CLI completions and natural language–to–bash translation in the command line.

Organizations also use Amazon CodeGuru Security to fix security vulnerabilities in code before it is released into production. Amazon CodeGuru Security detects, tracks, and fixes code security vulnerabilities anywhere in the development cycle using machine learning and automated reasoning.

Get started with AI testing on AWS by creating a free account today.