Enhancing Automation in QA Engineering with Advanced AI Techniques in Complex Distributed Systems

Abstract

Aims: This study explores integrating artificial intelligence (AI) into automated quality assurance (QA) workflows for complex distributed systems. Study Design: A multi-phase empirical approach was adopted. First, I developed a novel AI-driven test framework. Next, I deployed it in a real-world microservices environment and compared key metrics (defect detection rates, test coverage, execution time) against a conventional, manually-maintained QA suite. Place and Duration of Study: This work was conducted at the Department of Computer Science and Engineering, «Kharkiv Aviation Institute», from January 2024 to January 2025. Methodology: QA data (pass/fail results, defect logs, code coverage) were collected from 1,200 test cases spread across 15 microservices. An ensemble machine learning (ML) model (Random Forest + Gradient Boosting) was trained to predict modules with high defect probability. I integrated the AI-driven test prioritization algorithm into a Jenkins-based CI/CD pipeline. A series of 12 iterative production releases were monitored, capturing metrics like regression test time, defect detection, concurrency handling, and QA engineer feedback. The proposed ensemble machine learning model achieved an F1-score of 0.92, reducing missed defect rates by 32% and test execution time by 45%. Results: Test execution time reduced by 45% on average (from 110 minutes to ~60 minutes per full regression cycle). Escaped defect rate decreased by 32%, indicating more thorough coverage of high-risk areas. QA professionals reported a 35% increase in test efficiency and 20% fewer redundant test scripts. Concurrency issues (e.g., thread safety, race conditions) were detected 25% earlier in the QA cycle thanks to dynamic risk-based scheduling. Conclusion: AI-driven automation can significantly improve the speed and efficacy of QA for complex distributed systems, resulting in lower operational costs and more rapid release cycles. The proposed approach can serve as a blueprint for organizations seeking to modernize their QA pipelines with intelligent test orchestration.