Software Fault Prediction using Language Processing

Accurate prediction of faulty modules reduces the cost of software development and evolution. Two case studies with a language-processing based fault prediction measure are presented. The measure, refereed to as a QALP score, makes use of techniques from information retrieval to judge software quality. The QALP score has been shown to correlate with human judgements of software quality. The two case studies consider the measure's application to fault prediction using two programs (one open source, one proprietary). Linear mixed-effects regression models are used to identify relationships between defects and QALP score. Results, while complex, show that little correlation exists in the first case study, while statistically significant correlations exists in the second. In this second study the QALP score is helpful in predicting faults in modules (files) with its usefulness growing as module size increases.