AI Approaches to Software Quality Assessment: From Defect Prediction to Test Coverage Optimization

Abstract

The article addresses the problem of growing inefficiency in traditional approaches to software quality assurance (QA) under accelerated cycles of continuous integration and delivery (CI/CD). The aim of the study is to present and evaluate a new integrated platform that leverages large language models (LLMs) to automate and optimize key QA processes. The methodology is based on an industrial case study of a system utilizing Claude 4 and Amazon Q CLI for semantic log analysis and predictive test prioritization. The paper presents key quantitative results, including an increase in nightly build stability from ~70% to over 90%, a one-third reduction in regression testing time, and a halving of pull request verification time. Additionally, it is emphasized that the adoption of such solutions enhances testing transparency, improves collaboration between development and QA teams, and accelerates release cycles while ensuring higher product quality. The main contribution of the article is the provision of empirical evidence from a large corporate environment, confirming that modern AI-driven approaches can significantly improve the efficiency, accuracy, and strategic value of software testing. The article will be useful for researchers, quality engineers, CI/CD architects, and DevOps practitioners interested in applying LLMs to optimize testing processes.