Towards Generalizable Models in Software Failure Prediction: A Machine Learning Approach

Abstract

The inability to communicate effectively because of communication barriers severely restricts deaf and mute This paper highlights the challenges of generalizing models and dealing with contradictory evidence. Additionally, it explores the potential to enhance software failure prediction by integrating multiple research efforts. Traditional 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 strategies, and machine learning processes. The project includes exploring the use of different datasets and enhancing model training to accelerate 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 result of the findings.