Real-World Case Study: Boosting Test Coverage with GenAI Prompts

1. Real-World Case Study: Boosting Test Coverage with GenAI Prompts As software development cycles accelerate, QA teams are challenged to deliver broader test coverage in less time. This case study explores how a mid-sized SaaS company harnessed the power of Generative AI (GenAI) prompts to enhance their test strategy—boosting test coverage, speeding up test creation, and significantly improving product quality. Company Snapshot  Industry: Healthcare SaaS  QA Team Size: 12 members  Product: Patient Management Platform  Main Challenge: Achieving complete test coverage for complex workflows across modules with limited manual effort Despite having a solid automation framework, the QA team struggled to cover edge cases, maintain test consistency, and scale with growing product features. The Challenge 1.Incomplete Test Coverage: High-priority user flows were tested, but many edge cases and negative scenarios were missed. 2.Time-Consuming Test Creation: Writing detailed manual test cases was a bottleneck, consuming hours of QA effort. 3.Scaling Issues: With each new feature release, the team found it increasingly difficult to maintain comprehensive coverage across the platform. The GenAI Solution: Prompt-Based Testing To overcome these challenges, the QA team turned to GenAI prompt engineering. They used AI tools like ChatGPT to generate functional test cases from product specs, user stories, and logs. Implementation Workflow:  Crafting Prompts: QA engineers created prompts such as:

2. "Generate detailed test cases for the 'forgot password' workflow with both valid and invalid inputs."  Reviewing Outputs: GenAI returned a set of positive, negative, and edge-case scenarios, often highlighting paths the team hadn't considered.  Incorporating into Test Suites: Reviewed and validated test cases were added to automation suites and manual checklists.  Iterating Prompts: Prompts were refined over time with more details (e.g., roles, data types, validations) to increase relevance. Key Results Metric Before GenAI After GenAI Test Coverage 65% 92% Avg. Time per Test Case 30 minutes Under 10 minutes QA Team Efficiency Baseline +40% increase Missed Edge Cases Frequent Reduced by 70%  Higher Coverage: Broader scenario coverage led to earlier bug detection.  Faster Turnaround: Test design time dropped drastically, freeing QA resources.  Improved Onboarding: New testers quickly created useful test cases using GenAI guidance. What They Learned  Clear Prompts Drive Better Output: Specific, well-structured prompts produced more accurate and relevant test cases.  Human Review Is Still Essential: AI-generated content required QA validation for context and compliance.  Cross-Team Collaboration Helped: QA worked closely with product and dev teams to refine prompts for better scenario depth. Final Thoughts

3. This case study highlights how integrating GenAI prompts into QA workflows can elevate software testing. With the right approach, teams can cover more ground, find more bugs, and save valuable time—all while keeping up with rapid development cycles. magnitia Want to enhance your test coverage with GenAI? Start experimenting with structured prompts today and transform your QA process into a smarter, AI-powered engine.