Published Paper Details:

This paper explores the significance and methods of effectively utilizing historical data in software testing defect prediction. With the growing complexity of software systems, predicting and preventing defects has become paramount in ensuring software quality. Leveraging historical data, such as past defects and testing outcomes, can provide valuable insights into potential vulnerabilities and areas of improvement. The abstract delves into various approaches and techniques employed in harnessing historical data for defect prediction, including machine learning algorithms, statistical analysis, and data mining methodologies. Furthermore, it investigates the challenges and limitations associated with utilizing historical data in software testing, such as data quality issues, feature selection, and model validation. By synthesizing existing research findings and methodologies, this literature survey aims to provide a comprehensive understanding of how historical data can be effectively leveraged to enhance software testing defect prediction strategies, ultimately leading to improved software quality and reliability.