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5 Truths About AI-Driven Software Testing

As the world becomes more reliant on technology, the need for efficient and effective software development increases. One of the key aspects of software development is software testing, which is vital to ensure that applications and systems work as intended.

With the rapid advancements in machine learning (ML) and artificial intelligence (AI), AI-driven software test automation is proving to be a critical tool for businesses. AI can help automate software testing, driving significant time and cost savings while boosting overall productivity.

As businesses strive to enhance efficiency, accuracy, and agility in testing processes, understanding the truths about AI becomes crucial. In this blog, we delve into the five key aspects that shed light on the trends, risks, and benefits of incorporating AI in software testing.

1. AI-driven software testing improves software quality.

The first undeniable truth is that AI has the potential to significantly enhance software quality. With the ability to analyze vast amounts of data and identify patterns, AI-driven testing tools can detect defects, vulnerabilities, and performance issues that might be challenging for manual testing alone. By automating repetitive tasks and conducting comprehensive test coverage, AI contributes to a more robust and reliable software product.

Unlike manual testing, AI-driven tools can execute test cases at a remarkable speed, allowing for rapid feedback on code changes. This not only reduces the time-to-market but also ensures that new features do not introduce unforeseen bugs into the existing codebase. According to the 2024 Gartner® Market Guide for AI-Augmented Software-Testing Tools, “Software engineering leaders responsible for software quality and testing should maximize the value of AI-augmented software-testing tools by identifying areas of software testing where AI will be most impactful to the organization. For example, it may prove useful for generating test cases directly from user stories.”

2. AI can test for things we previously thought subjective.

A common roadblock on the path to automation is the inability for machines to handle some of the nuances of human perception. For user interface (UI) design in particular, most of the developer requirements are qualitative in nature. Attributes like usability, accessibility, and trustworthiness can all fall under this category.

However, AI has shown us that machines are able to model patterns, workflows, and tasks, including UI design testing. AI can test user interfaces, services, and lower-level components and can evaluate the functionality, performance, design, accessibility, and trustworthiness of applications. AI tools can analyze historical testing data, user behaviors, and system interactions to provide insights that inform more effective exploratory testing strategies. According to Gartner, the use of AI-augmented testing tools “continuously improves testing outcomes by learning from the data collected from performed activities.”

3. AI reduces the required maintenance for visual updates and redesigns.

The purpose of functional UI testing is just how it sounds – to confirm the functionality of an applications UI. For most web and mobile applications, functional UI testing can be challenging when using testing frameworks that rely on the document object model (DOM). DOM-based element selectors make tests susceptible to breaking because the structure and behavior of the UI changes with each update.

AI, and more specifically a branch of AI known as intelligent computer vision, gives us the newfound capability to perceive and test anything with a screen. Using image-based analysis, AI can recognize what appears on an application screen independently of how it is implemented. Without the need for DOM-based analysis, UI design changes do not result in excessive test script maintenance.

4. AI increases both the level of test coverage and test speed.

Traditional approaches to test automation consist of manually creating test cases for each new feature or application. This can take weeks or even months to complete, and there is a high level of risk with manual test case creation. Over time, the test coverage required to validate the quality of your software product diverges from the engineering team’s ability to design and write test scripts for it. Essentially, the software’s complexity is increasing faster than test automation can keep up.

AI-driven testing essentially narrows that gap between software complexity and test automation. This is especially beneficial in the enterprise space, where modern business applications need end-to-end testing and where time-to-market cycles are continuously shrinking.

5. AI-driven test automation is not a thing of the future; it’s already here.

We once thought tasks such as voice and image recognition, driving, and even musical composition were impossible for a machine to accomplish. But we’re already seeing this type of automation today. So it shouldn’t surprise you to know we are currently training AI bots to perceive, explore, model, and test software functionality.

As technology continues to advance, the capabilities of AI-driven testing tools will evolve, offering even more sophisticated solutions for QA professionals. Embracing AI in software testing is not just a trend; it's a necessity to keep pace with the increasing complexity of software applications and the demand for faster and more reliable releases.


With all the activity and buzz around AI for software testing, we are starting a new era of test automation. AI testing is enabling testers, developers, and all software professionals to tackle challenges that were once thought to be insurmountable.



For a complete look at the expanding role that AI plays in software test automation, I highly recommend downloading the digital book, Artificial Intelligence and Software Testing: Building Systems You Can Trust.

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