The field of software development is constantly evolving, and with the advent of artificial intelligence (AI), new tools and techniques are being developed to improve the quality of software products. In this essay, we will explore the use of AI tools for software quality assurance. Specifically, we will examine the use of AI-based test automation, AI-based defect prediction and prevention, and the limitations and challenges of using AI in software development and testing.

Figure 1. General software bug triaging process.

Best AI-based testing tools in 2024

Given the enormous improvements in machine learning and self-learning AI, AI-powered software testing tools have gotten significantly better over the past years. Below, we will delve into some of the top AI software testing tools available today.

1. Functionalize - Functionalize allows developers to execute end-to-end tests that are “self-healing” and capable of running at scale in the cloud. It promotes faster development, decreases expenses, and improves quality in continuous integration/continuous deployment (CI/CD) environments.

2. Testsigma – Testsigma uses AI to make test automation up to 5 times faster, allowing you to ship products with greater confidence. Empowering functional QAs to automate tests in plain English, Testsigma offers a platform to test web, mobile, and desktop apps, as well as APIs.

3. Katalon Studio - Katalon Studio prioritizes quality at the UI layer, using AI to eliminate false positives and deliver a seamless customer experience. It also offers visual testing capabilities that provide clear-cut ROI by saving significant human effort and time. Adding AI Visual Testing to existing Katalon Studio tests is a breeze, requiring no additional tests, and can be run parallel with functional tests for comprehensive coverage.

4. Applitools - Applitools represents the next generation of test automation platforms powered by Visual AI. This platform significantly reduces the time required for creating,executing, and maintaining automated tests, replacing traditional functional testing with AI.

5. TestCraft - TestCraft by Perforce offers a robust Selenium-based automated testing solution. This tool provides a codeless testing approach, enabling non-technical users to create and execute tests. With its AI/ML technology, it reduces the burden of maintenance while supporting remote work and collaboration. TestCraft balances speed, scale, and quality in web app delivery.

6. Testim - Testim aims to increase test coverage and reduce maintenance through its innovative AI-powered platform. It enables swift authoring, providing quality across applications, while its self-improving stabilizers eliminate flaky tests.

7. Watir - Watir is an open-source web application testing tool based on Ruby. It simulates real user interactions with web applications, supporting cross-browser automation testing. Lightweight and user-friendly, Watir provides simple, readable, and maintainable tests, making it a flexible solution for any web application.

8. Sauce Labs - Sauce Labs’s solution to the testing landscape is introducing low-code automated web testing services and empowering citizen testers to match the productivity of top-notch automation engineers. The tool's AI-powered systems enable authoring, writing, and full automation of test suites, even for those with little to no programming knowledge.

9. Eggplant AI - Eggplant Digital employs a model-based digital twin testing strategy; along with the power of AI, Eggplant provides vast coverage, enhances user experience, and prompts release cycles. With its easy-to-use interface, this tool offers a secure solution to test any software on any platform or device, significantly improving your quality assurance process.

10. ACCELQ - ACCELQ is a cloud-based, AI-powered codeless test automation platform. Its unified platform caters to a range of channels, from Web, Mobile, API, Desktop, to Backend. ACCELQ provides business process-focused automation integrated across the tech stack, enabling powerful handling of real-world complexities without requiring code.

Current Challenges for AI tools for QA

While AI-based tools have the potential to revolutionize software quality assurance, there are also limitations and challenges that must be addressed. One of the main challenges is the dependence on high-quality data sets for training AI models. Without good data, AI models may not be accurate or effective. Another challenge is the difficulty in interpreting and explaining AI-based results. Unlike traditional testing methods, AI-based tools may produce results that are difficult to understand or explain. This can make it challenging for developers to make decisions based on the results of AI-based testing. Finally, there are ethical concerns related to the use of AI in softwaredevelopment and testing. For example, AI-based tools may be biased or discriminatory if they are trained on data that reflects existing biases in society. Additionally, there may be concerns about the impact of AI on employment and job security in the software industry.

Conclusion

In conclusion, AI-based tools have the potential to improve software quality assurance by enabling more efficient and accurate testing, defect prediction and prevention, and other applications. However, there are also limitations and challenges that must be addressed, such as the need for high-quality data sets and the difficulty in interpreting AI-based results. As AI continues to evolve, it will be important for developers to carefully consider the benefits and limitations of these tools and to use them ethically and responsibly.

Anwarul Azim

Senior SQA Engineer, RiseUp Labs