Leveraging Machine Learning for Advanced Software Quality Prediction For Business Organizations

Abstract

In the realm of software engineering, ensuring high-quality software products is of paramount importance. Traditionally, software quality assurance relied on manual code reviews, testing, and debugging processes. Quality assurance teams followed established methodologies like Waterfall or Agile to manage the software development lifecycle. However, these methods had limitations in terms 
of predicting and preventing defects early in the development process. Additionally, they often lacked the ability to adapt to the rapidly evolving landscape of software technologies and architectures. This has led to the exploration of machine learning (ML) methods as a promising solution for predicting software quality, identifying defects, and improving overall software development processes.