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

Manual testing and limited automation were leading to repeated regression bugs and delayed releases.

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

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  1. Real-World Case Study: Boosting Test Coverage with GenAI Prompts In today’s fast-paced software development landscape, achieving comprehensive test coverage while meeting aggressive delivery timelines is a constant challenge. Traditional test case creation can be both time- consuming and error-prone. This case study explores how a mid-sized fintech company leveraged Generative AI (GenAI) to significantly improve test coverage using well-engineered prompts. Background The company was facing quality issues with their mobile banking app, primarily due to incomplete test scenarios. Manual testing and limited automation were leading to repeated regression bugs and delayed releases. The QA team decided to integrate GenAI into their test design process, aiming to expand coverage and accelerate script generation. The GenAI Approach The team adopted a strategy centered around prompt engineering. Instead of writing test scripts manually, they began feeding detailed prompts into a GenAI platform (like ChatGPT) to auto-generate test cases and automation scripts. For example, instead of saying: "Write test cases for login." They used: "Generate boundary value test cases, positive and negative, for a login screen with email and password validation, including error messages and user behavior under weak network conditions." Implementation Highlights 1.Prompt Libraries: A repository of reusable prompts was created for core features such as payments, login, and KYC verification. 2.Role-Specific Prompts: Test scenarios were generated for various user roles—admin, customer, support staff—to ensure role-based access coverage. 3.Edge Case Discovery: The team used prompts to uncover edge cases, such as timeouts, API failures, and browser incompatibility, which were previously missed. 4.Automation Integration: The AI-generated test cases were easily transformed into Selenium and Postman scripts, drastically reducing development time. Results Within 6 weeks of implementing GenAI in their QA process:  Test Coverage Increased by 42% The number of functional and non-functional test scenarios nearly doubled.

  2. Bug Leakage Dropped by 30% Fewer bugs made it to production, boosting customer satisfaction.  Test Design Time Cut by 60% What once took hours was now completed in minutes. Lessons Learned  Prompt Specificity Matters: The more detailed and contextual the prompt, the better the output.  Human Review Is Still Essential: While GenAI accelerates creation, expert QA review remains crucial to validate relevance and accuracy.  Iterative Refinement Works Best:Initial outputs weren’t perfect. Refining prompts over time led to better results. Conclusion This real-world case illustrates the transformative power of GenAI prompts in enhancing software quality. By thoughtfully engineering prompts and integrating AI-driven testing into the QA workflow, teams can boost efficiency, expand test coverage, and release with greater confidence. As GenAI continues to evolve, its role in quality assurance will only grow—especially for teams willing to harness its full potential through intelligent prompt design.

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