Abstract:

In software engineering, guaranteeing high-quality software solutions is essential. Historically, software quality assurance depended on manual code evaluations, testing, and debugging procedures. Quality assurance teams adhered to known approaches such as Waterfall or Agile to oversee the software development lifecycle. Nonetheless, these methodologies exhibited constraints regarding the early prediction and prevention of faults during the development process. Moreover, they frequently shown an inability to adjust to the swiftly changing environment of software technologies and architectures. This has prompted the investigation of machine learning (ML) techniques as a viable alternative for forecasting software quality, detecting faults, and enhancing overall software development processes. The necessity for a novel methodology in software quality prediction has become apparent owing to the growing complexity of software systems, stringent project timelines, and the market's desire for superior goods. Machine learning techniques provide a viable solution to these difficulties by utilizing past data, recognizing patterns, and generating predictions based on the acquired patterns. The necessity for precise, efficient, and automated software quality prediction methods has become essential for enterprises aiming to provide dependable software products. This research seeks to develop sophisticated machine learning models to enhance estimation accuracy by utilizing pertinent information from a substantial dataset. This study seeks to reconcile traditional software quality assurance methodologies with the growing requirements of contemporary software development through the application of machine learning techniques. This research aims to mitigate the identified problems to improve the software development process, resulting in superior software solutions and increased customer satisfaction.

Description:

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