Automating Regression Tests with Generative AI Models

Today’s enterprise IT teams are managing legacy systems while adopting new technologies. The constant arrival of new tech has created many opportunities for innovation and growth but has increased system complexity exponentially over the past decade.

Every new feature or update risks disrupting existing functionality, which means extensive regression testing efforts that are time-consuming and prone to errors. Traditional manual testing no longer keeps pace with the speed and complexity of modern software development cycles.

Now, more than ever, the time is ripe for a transformative technology like generative AI to reimagine regression testing and make it easier, faster, and more reliable.

Tech has grown, and so has system complexity

Several tech innovations have contributed to the modern-day IT environment’s complexity. Enterprise IT systems are no longer simple monoliths; they are complex webs of interconnected applications requiring frequent updates and maintenance.

Cloud computing has enabled companies to scale rapidly and access resources on-demand, but it has resulted in an explosion of data that needs to be managed and secured. The rise of microservices architecture encourages designing applications as smaller, independent services, each managed by different teams, adding complexity in communication and dependency management.

DevOps practices and Agile methodologies have further increased the pace of software development, requiring faster deployment and integration cycles.

Integrating new technologies introduces variables into software ecosystems, complicating the potential impact on system behavior. While these advancements lead to faster delivery and better user experiences, they also raise regression risks.

The opportunity for Gen AI in regression testing

Traditional regression testing entails manually creating test cases based on a small subset of core scenarios, often insufficient to cover the growing complexity and interactions across components.

In short sprint cycles, things move fast, and frequent changes mean that testers need to be flexible enough to adapt quickly. They need to trace what’s happening to manage complex dependencies, keep the team aligned, iron out any issues, and stay compliant—without any delays or errors.

When application updates need to be shipped twice a week, we’re looking at almost 100 opportunities for failure in a year.

Generative AI uses machine learning models to automatically generate test cases that maximize code coverage and find tricky edge cases manual testers might miss. Large datasets from user behavior, system logs, and code history help Gen AI tools simulate real scenarios and predict the impact of changes on applications.

The benefits are many…

Using Gen AI for regression testing offers many benefits, not the least of which is the time that gets unlocked by increasing test coverage:

• Gen AI tools automate test case and script generation by analyzing code, user requirements, and historical data to generate comprehensive test cases and scripts, while testers concentrate on more complex tasks.
• Gen AI provides self-healing capabilities that detect changes in applications and update test scripts, reducing maintenance efforts and ensuring continuous testing as applications evolve.
• AI also analyzes past results to prioritize test cases and helps eliminate redundancies, reducing execution time while maintaining high coverage.

At Sogeti, we are supporting our clients with the Gen AI Amplifier – a uniquely designed accelerator that provides pre-crafted prompts to speed up critical steps across the end-to-end quality engineering process in software development and modernization.

We’re quickly seeing our teams benefiting from integrating AI capabilities into their existing test automation frameworks with minimal effort and maximum impact – in the case of regression testing

… and concerns about Gen AI adoption are valid

Adopting generative AI in regression testing comes with its challenges:

• Compliance and data privacy are significant concerns, especially in industries with strict regulations. Ensuring AI models meet regulations while delivering value requires balancing AI capabilities with strict data protection standards.
• The black-box nature of AI can make it hard for stakeholders to trust AI-generated test cases and scripts. A lack of transparency can hinder adoption, and clear explanations are key to building trust. Explainable AI will help stakeholders understand test case generation and boost confidence in AI’s reliability.
• The unpredictability of AI outcomes can be daunting. While powerful, generative AI models pose a risk of unexpected results that can slow teams used to deterministic processes. Mitigating this requires rigorous testing and validation to ensure AI models perform as expected.

A little planning goes a long way

Much like traditional test automation, diving into AI-driven automation without a clear plan creates more problems than it solves, leading to disorganized testing processes and repositories filled with test cases that add little value.

Faster chaos is not the end game.

To effectively use generative AI in regression testing, we need clear goals and processes aligned with business objectives for measurable outcomes and optimal resource use. Gen AI should integrate into a well-structured test pyramid to maximize coverage, with a solid base of unit tests for cost-effective, scalable testing.

And it’s important to identify the right use cases and KPIs for AI-driven testing to produce meaningful results and justify the investment.

For example, using generative AI to cover low risk features with a high degree of variability would not provide significant value compared to using it for complex scenarios that require extensive manual testing. Or measuring, say, ‘test cases created per minute’ could provide a quick snapshot of the AI model’s efficiency and help point to areas for improvement.

When it comes to use cases, especially in regression testing, potentially the most important area is analyzing different versions of a requirement, identifying changes and incorporating test cases into the automation suite accordingly. Gen AI brings a lot of value here—by quickly generating test cases for multiple combinations and permutations. Combined with existing coding assistants, it could be integrated into the test automation IDE within seconds. The result: significant reduction in maintenance effort, improved coverage, and faster identification of bugs.

A key lever is maintaining traceability—linking each test case to specific requirements or features for alignment, analysis, and accountability. Notably, traceability is where generative AI makes things easier, with auto-generation of test cases for every requirement and feature, covering more ground with less effort.

To sum up: consider the long-term implications of implementing AI-driven regression testing. The initial effort may be high, but the potential benefits in terms of time and cost savings, as well as increased accuracy and coverage, make it a worthwhile investment.

A question to start off your Gen AI investment: what does smarter test coverage unlock for you?