Issues-Driven features for software fault prediction

Abstract

Software systems are an integral part of almost every modern industry. Unfortunately, the more complex the software, the more likely it will fail. A promising strategy is applying fault prediction models to predict which components may be defective. Since features are essential to the prediction model’s success, extracting significant features can improve the model’s accuracy. Previous research studies used software metrics as features in fault prediction models. One disadvantage of these features is that they measure the code developed rather than the requirements. On the other hand, faults are frequently the result of a mismatch between the software’s behavior and its needs.