Machine Learning-Driven QA Enhancement: Predictive Defect Analysis and Data-Driven Risk Management

Paper ID: EIJTEM_2024_11_4_58-61

Author's Name: Dr Mohammed Zaheer Ahmed, Dr Swarna Reddy

Volume: 11

Issue: 4

Year: 2024

Page No: 58-61

Abstract:

The increasing complexity of software systems necessitates advanced approaches to quality assurance (QA) that surpass traditional reactive methodologies. By leveraging the dataset compiled by Kothamali and Banik, this paper demonstrates how machine learning (ML) can revolutionize QA processes. ML algorithms, such as decision trees, support vector machines (SVM), and neural networks, can identify defect-prone areas, predict risk levels, and optimize resource allocation using historical QA metrics like code complexity, defect history, and developer activity. This data-driven approach enables development teams to proactively address potential issues, prioritize high-risk areas, and enhance testing efficiency. The paper also explores how unsupervised learning models like clustering and reinforcement learning can contribute to identifying hidden defect trends and refining testing strategies over time. Practical case studies and evaluations highlight the reduction in post-release defects, improved software reliability, and the integration of ML into agile and DevOps workflows. Furthermore, challenges such as data quality, model interpretability, and skill gaps are addressed, with proposed mitigation strategies for successful implementation. By providing actionable insights and a comprehensive framework for integrating ML into QA, this study paves the way for cost-effective, efficient, and smarter defect management in software development.

Keywords: Machine Learning-Driven, Predictive Defect Analysis, Data-Driven Risk Management

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