How Did The Role of AI and ML Evolve in Software Testing?

Mar 7, 2022
4 min read

A software test is a procedure that determines whether an electronic product meets standard requirements and whether it is defect-free. It entails using manual or automated tools to evaluate one or more properties of interest by executing software/system components. In contrast to actual requirements, software testing aims to find errors, gaps, and missing requirements. Software testing is necessary because it allows bugs or software errors to be identified early and corrected before the final product is delivered. A well-tested software product ensures dependability, security, high performance, saving time and improving customer satisfaction. When it comes to eliminating repetition and ensuring refinement, ML and AI are the technologies (cognitive) that emerge in our minds. AI and ML are causing a paradigm shift in software testing with deep learning-based algorithms. Various AI and machine learning (ML)-powered automation testing tools are now available on the market. These tools streamline software testing and allow teams to deliver more refined products.

In this article, we will talk about how AI and ML can be leveraged in the process of software testing and how their role is constantly evolving in the field.

The Utilization and Role of AI and ML in Different Stages of Software Testing

The three major stages of any software testing process are test case creation, analysis, and case maintenance. Let us look at how AI and ML play a role in software testing at various stages.

Test Case Creation

Test Case is a set of steps designed to ensure that your software application's specific features and functionality work as expected. For any test scenario, a test case consists of a series of test steps; data, preconditions, and postconditions. Testing engineers use test cases to compare expected and actual results in order to determine whether a software product meets the needs of their customers.

You can write the cases manually while the system automates them using AI and Ml technologies. You can use automatic test case creation to identify and create test cases without human intervention. This can be used for both visual and functional testing. The use of AI and machine learning to generate test cases is still in its early stages. With more research, the future appears bright, and we will have more efficient and accurate test case creation methods.

Test Case Analysis

You can use machine learning and computer vision to conduct visual testing of your test cases for test case analysis. You can use this information to compare your current product version to previous versions and make necessary changes. Using AI and ML, you could, for example, identify high-risk parts of the source code and communicate this information to the QA engineers. Then, before deploying the applications, the developer would keep this in mind, and your test cases would be more reliable.

Test Case Maintenance

It is essential for manual testing teams to constantly monitor all test cases and modify them in accordance with the current features of the system. Users provided input from manual testing activities in the traditional test management and maintenance system. However, you can now use AI and machine learning to automate testing, automatically pushing the results into test management and maintenance systems. This is because AI and machine learning-based testing rely on the self-learning model. As a result, if the current system undergoes any changes, it detects them and updates the test cases in real-time.

Approaches to AI and ML Implementation in Software Testing

There are different approaches to AI and ML implementation in software testing. Some of them are -

Visual Testing

Visual testing or user interface testing is a way for developers to ensure that a web or mobile app's UI meets the original requirements. It accomplishes this by using AI and ML-based image processing and screen comparison algorithms to test a website or application's overall look and feel. Percy is one popular tool for visual testing.

Differential Testing

The objective of differential testing is to observe the differences between similar applications by applying the same input. Coding-related issues, security vulnerabilities, regressions, and other issues can be found using AI and ML-based self-learning algorithms. It's one of the newest methods for detecting changes.

Self Healing Automation

Self-healing test automation tools use AI and machine learning techniques to update changes in an application's environment automatically. It monitors changes in the UI and adjusts the test cases accordingly. It also aids in the elimination of the need for test case upkeep. The technology allows tests to adapt and update themselves. Mabl and Testim are two famous automation tools.

Declarative Testing

Declarative testing concentrates on what you want to achieve with this test rather than how the process will work. It's more important to figure out what you're testing for than it is to figure out how you're going to test. It also allows you to compare the app's final state to the expected state. This testing aims to automate tedious, error-prone, and repetitive tasks to boost productivity.

Benefits of Integrating AI and ML in Software Testing

Visual Validation

Pattern recognition and image recognition capabilities in AI aid in detecting visual bugs in applications through visual testing. It aids in ensuring that all of the visual elements are engaging and functional.

Accuracy

Manual testing results in a lot of human error, especially when repetitive tasks are required. Automation testing eliminates these human-caused errors. However, with the help of artificial intelligence (AI) in automation testing, repetitive tasks are handled more efficiently, and accurate results are recorded.

Saves Time and Money

When there is a change in the source code, software tests must be repeated. Manually, this becomes time-consuming and requires a significant amount of effort from testers. On the other hand, repetitive tasks are handled correctly, quickly, and efficiently with AI-driven tests.

Decreased Defect

AI and Ml help in the early and rapid identification of bugs, reducing defects, and increasing product reliability.

Conclusion

With cutting-edge technologies like AI and machine learning, software testing has taken on a whole new meaning. It is becoming more precise and efficient than it has ever been. Software testers no longer have to deal with the same tasks repeatedly. Instead, they are responsible for defining procedures and establishing a testing road map. The primary goal of incorporating AI and machine learning into the testing ecosystem is to automate repetitive tasks that waste manual time and effort.