WeTest.Athens 2026

Analyzing requirements and translating them into test scenarios and test cases is a core activity for QA engineers. However, identifying meaningful edge cases often requires deep product knowledge and an understanding of how the system is implemented something that is not always available during early analysis, especially for teams that are scaling.
This workshop explores how AI can assist testers during requirement analysis. Using the same specification, we compare two approaches: a general-purpose AI model (such as Gemini) with no access to the system, and an AI tool connected to the project repositories (such as Amazon Q). By comparing the results, we examine how system context influences the depth and relevance of generated test cases, and how AI can help testers discover scenarios that might otherwise require extensive product familiarity.