Stepaniuk, Taras2026-06-082026-06-082026-05Stepaniuk, Taras. (2026). AI INTEGRATION FOR TEST CASES GENERATION AND MAINTENANCE: OPTIMIZING TEST TEAM WORKFLOW. Kyiv: American University Kyiv. URI:https://er.auk.edu.ua/handle/234907866/187Manual quality assurance workflows in fast-paced software delivery increasingly struggle to keep pace with rapid code evolution, with QA teams spending disproportionate effort on interpreting requirements and maintaining test documentation. This research investigates whether an Artificial Intelligence-driven middleware can optimize this workflow by automating the synthesis of requirements, design, source code, and existing test documentation into actionable testing artifacts. A middleware solution was designed and implemented on the Fastlane framework, integrating data from Asana, Figma, GitLab, and TestRail, and leveraging the OpenAI GPT-4.1 model through a structured Chain-of-Thought prompt. The evaluation combined quantitative KPI tracking across 24 production tasks with qualitative feedback from three QA engineers. The results demonstrate a 68% reduction in the test documentation effort ratio, a decrease in the median number of dev/test iterations from three to one, and a doubling of the single-iteration resolution rate. Qualitative analysis confirmed accelerated feature comprehension and reduced cognitive load. The study validates a hybrid human–AI model of quality assurance and defines a roadmap toward autonomous test maintenance.en-USartificial intelligencesoftware testingtest case generationquality assuranceCI/CDmiddlewareThesis