AI in Software Quality Assurance: A Review of Machine Learning for Testing, Bug Prediction, and Debugging
International Research Journal of Modernization in Engineering Technology and Science · 2025
Rapidly advancing software systems along with product demand for high-quality reliable applications now make Software Quality Assurance (SQA) an essential practice.Modern testing and debugging techniques face difficulties in staying synchronized with short development periods along with large modern code bases.This paper investigates how Artificial Intelligence (AI) especially Machine Learning (ML) methods improve SQA processes by refining software testing along with bug prediction and debugging systems.This work analyzes the use of supervised and unsupervised and deep learning models for automation and enhancement across various quality assurance domains.The integration of machine learning algorithms transforms testing by enhancing the methods which generate test cases and detect faults and prioritize their execution for improved efficiency.Software developers use bug prediction models based on historical code repositories and software metrics to determine defect-prone modules so they can conduct proactive quality control in early development stages.Through AI technology the process of debugging benefits from automated fault localization with root cause analysis capabilities that eliminate large amounts of manual work.Data quality and model interpretability alongside domain-specific training datasets represent essential difficulties which are analyzed throughout the review.This study presents a review of contemporary research coupled with industry patterns to demonstrate AI-driven SQA tools' increased sophistication and emphasize unexplored areas of study.Software engineering embraces innovative solutions because AI and ML support the enhancement of existing quality assurance methods while implementing intelligent adaptive scalable solutions into the professional landscape.