Revolutionize your software testing : AI, the Secret weapon for QA teams this fall 2024

Software test automation is rapidly evolving and transforming with the growing adoption of artificial intelligence (AI). As companies strive to reduce the time-to-market for their applications and improve product quality, AI has become a must-have. According to the Gartner Market Guide for AI-Augmented Software-Testing Tools 2024, 80% of companies will have integrated AI-augmented testing tools into their software engineering processes by 2024, compared to only 15% in 2023. AI not only speeds up testing but also improves precision and coverage.

In 2023, the global market for AI-based test automation tools was valued at $3.5 billion, and it is expected to double by 2026. Companies that integrate AI into their testing processes could see up to a 50% reduction in testing costs and a productivity gain of up to 30%.

QA : How is test automation revolutionized by AI ?

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Originally, testing was manual, performed by “human” testers who had to identify bugs and other malfunctions in the code. It was a slow, labor-intensive process, prone to errors, and difficult to repeat. Test automation emerged as THE solution to improve the reliability and speed of this essential phase of software quality. Test automation has evolved from simple action reproduction to more sophisticated tools like Selenium, enabling more robust testing but still requiring manual maintenance. With the rise of CI/CD pipelines, end-to-end automation has become essential, integrating functional, performance, and security testing. However, even with these advancements, human oversight remained necessary to manage complex scenarios, until the advent of AI significantly improved testing efficiency and coverage.

Today, AI enables smarter and more adaptable automation. Advances in machine learning algorithms and natural language processing have allowed testing tools to evolve far beyond simple automated scripts.

• Predictive testing and improved quality : AI also enables predictive testing, where algorithms can identify potential weaknesses before they manifest, increasing software quality from the early stages of development.
• Autonomous testing : The ability to automatically generate test cases, detect anomalies, and identify the areas of code most likely to contain bugs. For example, AI can analyze historical test data to predict where issues are most likely to occur and focus testing efforts on those areas.
• Cost and development time reduction : Automation of complex tasks such as test coverage analysis and test suite optimization for faster problem identification.
• Adaptability and reduced maintenance : Unlike traditional automated tests that require frequent maintenance due to code or interface changes, AI systems adapt to changes in real time. Tools like Testim or Applitools use AI to dynamically adjust tests based on application changes, reducing the need to rewrite tests.

Which tools to test this fall and adopt in 2025?

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According to the State of Software Quality Report 2024 by Katalon, test case generation is the most commonly used feature in AI-enhanced testing tools. They accelerate test cycles by drastically reducing manual interventions. Admittedly, some complex scenarios are not yet eligible, but AI helps testers create increasingly comprehensive and efficient test cases.

Moreover, generative AI produces a wide range of test data, allowing software to be tested under various conditions, saving time and effort.

Here is a selection of tools to integrate into your test automation stack :

• Testim stands out for its use of AI for automatic test maintenance, thanks to “smart locators” that adapt to code changes. This tool improves test quality by enabling faster creation and reducing errors in agile projects.
• Applitools specializes in visual testing, detecting subtle anomalies in user interfaces with its AI engine “Eyes.” This service ensures impeccable visual quality across multiple platforms and devices, crucial for visually intensive applications.
• Functionize takes a no-code approach to creating automated tests, using AI to simplify the process for non-technical users. Its great flexibility and scalability make it an ideal choice for large enterprises.
• BrowserStack, a cloud platform, allows non-technical users to create, run, and manage tests without coding, thanks to AI features such as self-maintenance and automatic test generation.
• Katalon Studio offers a GPT-based environment for developing automated test cases. It enables teams to quickly identify error causes through its virtual data analysis and improve test stability.
• Selenium IDE, well-known for its simple interface and multi-platform support, allows the creation of automated tests while recording user interactions.
• Testsigma allows the creation of automated tests using natural language phrases, simplifying the creation of complex tests and reducing maintenance efforts by 70% thanks to its self-healing capabilities.
• Tricentis also has a no-code approach, suitable for non-technical users and test engineers, offering AI insights to minimize test failure risks and improve collaboration.
• ACCELQ offers no-code automation for web and mobile applications, reducing maintenance by 72% and lowering automation costs by 53%.
• TestRigor uses generative AI to convert plain English texts into automated test scripts, facilitating test automation for QA teams.

Challenges to overcome when integrating AI into your testing process

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Integrating AI into your existing testing pipelines can be challenging. Companies often need to adapt their testing infrastructures to fully benefit from AI tools, which may require significant investments in time and resources. Compatibility with traditional testing tools and interoperability with existing CI/CD pipelines are also major concerns. Adding AI capabilities to testing processes can introduce additional complexity, requiring workflow reconfiguration and rigorous management to avoid disruptions.

Adopting AI-based testing technologies requires new and varied skills within QA teams. Testers must not only master traditional software testing concepts but also acquire knowledge of AI and machine learning. This dual skill set is essential to understand how AI tools work, how they generate tests, and how to interpret the results provided by these systems. At Harington, we have these skills and put them at the service of our clients. Feel free to reach out to us for your projects.

Ready to go further in test automation?

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AI plays an increasingly crucial role in software test automation, improving the efficiency, accuracy, and coverage of tests. Tools like Testim, Applitools, and Functionize demonstrate how AI can transform the testing process, making tests more adaptable, accessible, and reliable. In the future, advances in generative AI and machine learning will enable even more comprehensive automation, with systems capable of autonomously managing complex test scenarios and adapting to emerging technologies. Are you ready to adopt AI in your testing process today ?

Our QA experts are here to guide you to success.