Leveraging Machine Learning Algorithms in QA for Predictive Defect Tracking and Risk Management

Abstract

In the dynamic and rapidly evolving landscape of software development, maintaining high
standards of quality assurance (QA) and effectively managing risks are essential to achieving
project success and sustaining software reliability. Traditional QA methods often rely on reactive
approaches, addressing defects only after they have occurred, which can lead to increased costs,
delayed timelines, and compromised software quality. This paper explores the transformative role
of machine learning (ML) algorithms in enhancing QA processes, particularly in predictive defect
tracking and risk management. By harnessing the power of historical data, ML models can identify
hidden patterns in the software development lifecycle, predict potential defects before they
manifest, and assess risk levels across various components. This proactive approach enables
development teams to prioritize high-risk areas, optimize testing strategies, and allocate resources
more efficiently. The study evaluates different machine learning algorithms—such as logistic
regression, random forests, support vector machines, and deep learning models—and compares
their effectiveness in predicting defect-prone code areas and managing software vulnerabilities.
Furthermore, the integration of machine learning into QA workflows not only reduces post-release
defects but also improves the overall software development process by automating test case
generation, defect prioritization, and continuous risk monitoring. The paper also discusses the
challenges and limitations of implementing ML in QA, such as data quality, model interpretability,
and the need for specialized skills, along with proposed mitigation strategies. Ultimately, this
research demonstrates that adopting machine learning for predictive defect tracking and risk
management in QA can lead to significant improvements in software reliability, reduced testing
costs, faster development cycles, and enhanced project efficiency, paving the way for smarter,
data-driven software quality management practices.