Machine Learning Models for Software Defect Detection: A Strategic Management Approach

Abstract

Businesses are at serious risk from software flaws, which can affect consumer trust, security, and operational effectiveness. Proactive quality assurance is made possible by machine learning's (ML) creative approaches to software defect detection and prediction. From the standpoint of strategic management, companies can manage the allocation of resources efficiently, increase decision-making, and improve overall software quality by incorporating machine learning (ML) models into defect detection procedures. This study examines the business ramifications of AI-driven quality assurance as well as the different machine learning models used for software defect identification and their incorporation into IT management plans. The paper emphasizes ML's significance in long-term strategic planning and operational efficiency alongwith highlighting the difficulties, advantages, and potential future directions of using it for defect management in organizational settings.