It all depends on how you use it. Sorry to everyone looking for a simple “yes” or “no,” but let’s be honest: it’s all much more complicated than this. Some believe a Gen AI assistant is a magic pill, ready to answer all of your questions and automate most of your processes. Others are certain that AI, in its present form and the way people treat it, is another bubble. All that’s left is to wait and see.
But it doesn’t mean you must ignore the trends and stay away from ChatGPT and Co. Just don’t put too much trust in it—not yet. Generative AI is a technological breakthrough that’s reshaping how product teams approach tasks—with testing among those. If you can complete something more effectively with less time, why not use that opportunity? The key is in the right approach, which should involve lots of critical thinking and fact-checking.
Let’s take a closer look at generative AI and software testing. In this article, we’ll cover the opportunities and limitations that generative AI introduces in software quality assurance and how to navigate them.
As software evolves, so do the development challenges. Yes, it’s a beaten phrase, but it’s a good start for a conversation about software testing with generative AI.
So, digital products get more complex. Competition gets more fierce. Users get more demanding. Deadlines get tighter. No wonder that tech teams face challenges traditional testing approaches struggle to address effectively.
Among other things, QA engineers need to handle:
These are just a few top-of-mind examples that the majority will probably find relatable. And that’s why we start moving from “how to test AI applications” to “how AI applications can test.”
No wonder teams turn to generative AI, seeking a more effective or affordable alternative for testing. But it’s not the equivalent of a skilled QA engineer. And a QA engineer who knows how to use Gen AI tools still doesn’t equal a dedicated QA team.
According to the Capgemini’s annual quality report, 34% of organizations are actively using Gen AI in their quality engineering and testing processes. The same amount is building roadmaps after initial experiments. 9% are planning to start soon, and only 4% aren’t exploring Gen AI solutions. Those familiar with LLMs report having risks and discovering new challenges.
Biggest risks of using generative AI:
Biggest challenges of using generative AI:
4% of the respondents report no challenges preventing or complicating Gen AI adoption.
According to the report, most organizations are currently experimenting with Gen AI applications to identify what are delivering the most benefit. For the majority—56% of respondents, to be accurate—generative AI isn’t the means to keep a competitive edge. It’s not even a tool to reduce defects and cut costs. So what is generative AI for quality assurance? It’s a tool for boosting productivity of quality engineers.
Back to square one, it’s all about how you use generative AI for testing. If you intend to replace human expertise or build a QA framework without involving quality assurance engineers, you might want to reconsider.
Basically, Gen AI is the means to take better care of your team. That might sound weird to some. Yet, getting more done in less time ultimately drives better productivity, faster releases, and broader coverage. It also leaves your QA engineers motivated rather than exhausted.
Capgemini sums it up with three recommendations:
These rules will help you avoid common traps, like trying to “facilitate” tasks that are actually easier to perform manually—given the possible glitches and lack of context.
Let’s get to the practical part: generative AI use cases in software testing. those span the entire testing lifecycle—from requirements creation through test execution to defect analysis and reporting. The following are the most likely Gen AI use cases for implementation—from most to least likely, as discovered by Capgemini.
Generative AI can help organize and summarize test results in more accessible formats. Rather than simply listing pass/fail statuses, Gen AI can identify patterns and present findings in clearer language.
For example, when analyzing a test run with multiple failures, an AI system might group related issues and highlight common factors like, “5 failures occurred in the payment processing module after the recent API update.” This saves QA engineers time in manual analysis and helps stakeholders understand the impact more quickly.
These tools still require human review and interpretation, especially for complex issues or when determining business impact. The AI provides a starting point that testing teams can refine rather than a complete replacement for human analysis.
AI tools can assist with defect triage by comparing new bugs against historical data. This helps with initial classification and prioritization, though with varying accuracy depending on the quality of historical data.
When a new defect is reported, AI might suggest similar past issues, potentially helping developers identify fixes more quickly. For instance, it might note, “This validation error appears similar to three bugs fixed in the user management module last quarter,” giving the team a starting point for investigation.
The analysis is more suggestive than definitive. Experienced QA engineers and developers still need to evaluate these suggestions and often discover connections or causes the AI missed. The technology works best as an assistant rather than the primary analyst.
Generative AI for QA testing can help organize documentation and make it more accessible through improved search capabilities and summarization features.
When a QA engineer needs information about testing a specific feature, AI can pull relevant content from test plans, bug reports, and documentation. Instead of searching through multiple documents, a person might ask, “How do we test the two-factor authentication?” and receive synthesized information from various sources.
The quality depends heavily on the existing documentation. AI can’t create knowledge that doesn’t exist, and information retrieval sometimes misses context or nuance that human experts would catch. Teams still need to maintain good documentation practices for the AI to be effective.
Generative AI can help create test data sets that mimic production data patterns while avoiding privacy concerns. This is particularly useful for performance testing and scenarios requiring diverse data.
For instance, for a retail application, AI might generate orders with realistic product combinations and seasonal patterns without copying actual customer data. This synthetic data helps test system behavior under various conditions while remaining compliant with data protection regulations.
The quality varies by domain complexity. Generating simple user profiles works fairly well. Meanwhile, complex relational data with intricate business rules often requires additional validation and adjustment by testing specialists.
AI tools can assist with converting test scripts between similar frameworks, though—again—with limitations. They work best when moving between related technologies with similar paradigms.
When migrating from one Selenium-based framework to another, AI might help translate the core test logic and selectors. This reduces some manual effort but doesn’t eliminate it. Humans must review and adjust the converted scripts, especially for framework-specific features or best practices.
Complete, error-free conversion remains challenging, particularly for complex test suites or when moving between significantly different testing approaches. The technology serves more as a helpful starting point that still requires testing expertise to refine.
AI can analyze test execution patterns to identify potential redundancies and coverage gaps. These analyses typically provide suggestions rather than automated fixes.
An AI tool might flag that certain login tests consistently pass together or fail together, suggesting they may be testing the same functionality. Or it might be noticed that error handling paths rarely execute during testing, indicating potential coverage issues.
These insights require interpretation by testing experts. They have contextual knowledge about why certain tests exist or why coverage patterns appear as they do. The AI provides observations to save some time while an engineer evaluates them for potential optimization.
Generative AI can help map requirements to existing test cases. It’s possible by analyzing textual similarities. The connections, however, aren’t always precise and require fact-checking.
When analyzing a financial application, AI might identify that certain regulatory requirements have corresponding test cases while others appear to lack verification. This gives test managers a starting point for coverage analysis. They move straight to validating these connections.
The accuracy depends heavily on how well-written the requirements and test cases are. Vague requirements or generic test descriptions make it difficult for AI to establish meaningful connections, limiting the usefulness of the analysis.
AI can suggest improvements to requirement drafts by identifying ambiguities and inconsistencies. Again, it doesn’t replace the domain expertise needed for proper requirements definition, but it facilitates the overall task.
When reviewing requirements, AI might flag statements like “The system should load quickly” and suggest more measurable alternatives like “The dashboard should load within 5 seconds.” These suggestions help teams write more testable requirements. Stakeholders still need to determine the actual business needs, but they get a better idea of what to edit and how to do it.
The technology works best as an editing assistant rather than creating requirements from scratch. It can help with clarity and format consistency but lacks the contextual understanding of business priorities and technical constraints.
Generative AI in QA automation can help draft test scripts based on natural language descriptions. The results typically require significant review and modification by automation QA engineers. Also, it may be more reliable to use actual test cases for manual testing to create effective scripts (that’s what automation experts normally do).
Here’s one way to apply it. An AQA engineer might describe a scenario like “Verify that admin users can create new accounts,” and the AI could generate a starting script that navigates to the admin panel and attempts the action. This can help specialists who are less familiar with programming to begin creating automation.
The generated scripts often contain assumptions about the application structure and selectors that need correction. They serve more as templates that accelerate test creation rather than production-ready automation that works immediately without adjustments. It may be quicker to proofread and edit such scripts than to write them from scratch, but you’ll need to check and see.
AI can suggest potential test scenarios based on requirements or application interfaces. However, there can be issues with varying coverage and quality of such documentation. In other words, it all still requires human review.
For a new feature described in requirements, AI might propose test cases covering the main functionality, some edge cases, and error scenarios. This gives QA engineers a starting point that they can then refine, expand, or prioritize based on their understanding of business risks and technical implementation.
The suggested tests often cover obvious scenarios well but miss subtle business rules or complex interactions that experienced testers would identify. That’s why they’re most useful as a complement to human test design rather than a replacement for testing expertise.
The versatility of generative AI tools for software testing keeps growing. Generative AI for automation testing is probably the first thing that comes to mind, but the features and offerings are already diverse.
Companies like Testim, mabl, and Applitools utilize AI and improve test creation and maintenance. Meanwhile, platforms such as Functionize and Appvance incorporate machine learning to make testing more resilient to application changes.
Open-source tools like Diffblue Cover automatically generate unit tests for Java code. Commercial offerings such as GitHub Copilot and Amazon CodeWhisperer assist developers with test creation through code suggestions.
Altogether, this already helps cover a variety of testing types and requests. And it’s possible as companies don’t forget about AI testing services for these innovative tools on their side.
You can learn more about some best AI automation testing tools in one of our previous articles.
Starting small represents the most effective approach for implementing generative AI in testing. Teams should identify specific cases where Gen AI will enhance efficiency and reduce the time for task execution. And it will vary from team to team. The only way to determine what works for you is to let QA engineers try and see. In other words, give them room for experiments and don’t wait for a magic efficiency boost from the first and every single attempt.
Integration with existing testing frameworks is crucial for successful implementation. Generative AI in testing should complement rather than replace current practices. This integration ensures that accessibility testing becomes a natural part of the development process rather than a separate activity.
Measuring success requires appropriate KPIs for generative AI in testing. Set metrics based on the goals and objectives for Gen AI. This list can include, for example:
Building team capabilities around AI-assisted testing involves educating team members about generative AI’s potential and limitations. This education should include both technical aspects of using the technology and the business value it delivers.
Finally, creating a feedback loop between AI-generated tests and QA engineers helps refine and improve the technology’s effectiveness over time. This collaborative approach ensures that the AI continues to learn from human expertise while providing increasingly valuable assistance for testing.
Assessing your organization’s AI readiness is the first step toward implementing generative AI for software testing. Consider your current processes, team capabilities, and specific QA challenges. Identify where generative AI can provide the most immediate value.
Start with pilot projects focused on the defined challenges. Gradually expand to more comprehensive applications. The phased approach will enable your team to build confidence and expertise while delivering tangible benefits at each stage.
Building a long-term vision for QA transformation means thinking beyond immediate tactical applications. Consider how generative AI might fundamentally change your approach to QA. This vision should align with your overall product strategy and business goals.
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