IMPROVING SOFTWARE FAULT PREDICTION THROUGH CROSS-PROJECT ANALYSIS A FOCUS ON IMBALANCED DATA AND GENERALIZATION

This paper delves into the challenges of generalizing models and dealing with contradicting evidence. Additionally, it delves into the potential for enhancing software failure prediction by integrating several research endeavors. Conventional methods of failure prediction could not be highly task-specific due to the fact that not all tasks had access to the same data. To overcome these challenges and achieve better prediction accuracy, you can employ feature selection techniques, data resampling tactics, and machine learning procedures. The project involves exploring the usage of various datasets and enhancing model training to expedite problem detection and ensure that solutions are compatible with different software configurations. Software quality assurance methods can be improved and made more adaptable as a direct consequence of the findings.