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Software testing is both hard and necessary. Luckily, AI is here to help, from predicting bugs to generating test cases. Here, an experienced software tester explains how.
Every app, website and smart device we use today relies on software that works reliably. Within the development lifecycle, testing is the guardian of quality, ensuring systems do what they should and avoid what they shouldn’t, whether that means crashing, leaking data or frustrating users.
Testing has always been challenging, but the shift from large, monolithic projects to incremental, agile, continuous delivery over the past decade or so has intensified the strain. Automated testing is a powerful approach to dealing with the increase in pace. However, modern systems are updated constantly, span multiple devices/micro-services and connect to numerous external systems. With so many combinations of features, data and environments, testing everything is impossible.
As development cycles shorten and releases become more frequent, the burden on testers grows. That’s where artificial intelligence (AI) is beginning to make a difference.
AI is often used as a buzzword, but in software testing two main flavours matter.
Machine Learning (ML) excels at finding patterns in complex data. Trained on past defects, test logs, code metrics and more, ML models can predict where bugs are most likely to appear. They can also analyse results to spot patterns.
Generative AI (GenAI), powered by Large Language Models (LLMs), extends this capability by learning from language patterns to generate new content, such as test cases, scenarios and automation code.
In testing, ML helps decide what to test and analyses results, while GenAI helps create the tests themselves. Both rely on data-driven models but serve distinct purposes: one predicts and analyses, the other produces.
One of the earliest steps in testing is identifying what needs to be tested. AI can now analyse requirements or user stories and propose potential scenarios automatically. It can flag new functionality, generate likely edge cases (“what if two users try to access the same record simultaneously?”) and present a structured list of tests, giving teams a faster, more complete starting point.
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Once scenarios are defined, writing automation scripts is time-consuming. GenAI tools are able to convert plain English prompts (“log in, change address, verify confirmation”) into executable code in Python, Java or tool-specific languages. Testers still need to review and refine these scripts, but they gain a significant head start and spend more time on validation rather than boilerplate coding.
In agile and continuous delivery environments, there’s rarely time to run everything. ML models can be used to analyse data such as defect density, module churn and test outcomes to predict where defects are most likely to occur. This powers risk-based testing, allowing teams to prioritise high-value tests and defer low-risk ones, improving speed without sacrificing confidence.
AI also helps interpret the flood of data after each test run. Tools can group similar failures, identify flaky automation tests (ones that fail incorrectly due to test automation, data or environment issues), summarise trends and link defects to recent code changes. The result is faster triage, fewer false positives and deeper insight into recurring problem areas, strengthening the feedback loop between testing and development.
AI is already built into many testing tools. Microsoft’s GitHub Copilot helps engineers write Selenium or API test scripts from natural-language prompts. Platforms such as Tricentis Tosca, OpenText UFT One and Leapwork use AI for image and object recognition, letting tests self-heal when interfaces change and for translating natural-language scenarios into automation flows.
Original Software also applies AI within its testing suite, using intelligent object recognition to maintain test stability across browsers and devices. Its analytics tools highlight risky code areas and duplicate defects, helping teams focus on what matters most.
Across all these examples, AI acts as an assistant, not a replacement. It handles repetitive or data-heavy tasks and surfaces insights fast, while human testers apply judgement, creativity and business context to ensure the software truly meets user needs.
AI certainly isn’t infallible. It can misunderstand vague requirements, generate brittle tests or reinforce bad habits if trained on flawed data. Self-healing automation may fail when interfaces change significantly and ML predictions are only as reliable as the data behind them.
Crucially, AI can’t understand business risk or user experience, areas where human insight remains indispensable. The best teams treat AI as a productivity partner: fast and tireless, but always subject to review. Every AI-generated artefact needs human review and critical thinking to identify gaps.
As GenAI and ML become more context-aware, they’re moving from assistants to collaborators. Imagine an AI agent that monitors every code change, generates relevant test cases, executes them, analyses the results and flags risks for review. That may sound futuristic, but many building blocks are already here; and the term “agentic AI” is the latest buzzword in technology.
However, this evolution raises important questions about trust and accountability. If an AI-driven system misses coverage or wrongly predicts low risk, who is responsible? As organisations adopt AI, governance, transparency and oversight become critical.
Ultimately, the future of testing isn’t human or AI – it’s human and AI. By combining human creativity, domain expertise and ethical judgement with machine speed, scale and consistency, teams can deliver higher-quality software faster and with greater confidence.
