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. The need for an innovative approach to software quality prediction became evident due to the increasing complexity of software systems, tight project schedules, and the demand for high-quality products in the market. ML methods offered a promising avenue for addressing these challenges by leveraging historical data, identifying patterns, and making predictions based on the learned patterns. The need for accurate, efficient, and automated software quality prediction techniques became critical for organizations striving to deliver reliable software products. Therefore, this research aims to build an advanced ML models to improve the estimation accuracy with the usage of relevant features of a large dataset. Further, this work aims to bridge the gap between traditional software quality assurance methods and the evolving needs of modern software development by employing machine learning techniques. By addressing the aforementioned challenges, the research endeavors to enhance the overall software development process, leading to higher-quality software products and improved customer satisfaction.