Precision Software Defect Prediction Using Novel Machine Learning Approaches
TL;DR
This study aims to improve software defect prediction five publicly available NASA datasets by using Random Forest and Classification Network to achieve higher defect prediction accuracy compared to methods without feature selection (WOFS) and to get matrix problem the authors use Classification Network.
Abstract
Abstract - Software defect detection is a crucial area in software engineering focused on identifying issues within software systems. To achieve software success, it is essential to bridge the gap between software engineering and data mining. Various methods, including clustering, statistical techniques, matrix-based neural networks, black box & white box testing, & machine learning, remain employed toward predict software defects. These methods are applied to enhance accuracy in defect prediction through machine learning. Objective of this study is toward improving software defect prediction five publicly available NASA datasets: CM1, JM1, KC2, KC1, and PC1. Feature assortment performances are cohesive through ML methods, such as Random Forest to achieve higher defect prediction accuracy compared to methods without feature selection (WOFS) and to get matrix problem we use Classification Network. A defect is defined as an imperfection caused by an error, fault, or failure in the software development process. In this context, an "error" refers to human actions leading to undesirable outcomes, while a "defect" denotes a decision resulting in incorrect outcomes when attempting to resolve a problem. Software defect prediction has gained prominence in recent years due to its direct impact on software quality. Defective software modules can affect product quality, leading to cost overruns, delays in project timelines, and increased maintenance expenses. Key Words: software defect prediction five publicly available NASA datasets: CM1, JM1, KC2, KC1, and PC1.