Software metrics for fault prediction using machine learning approaches: A literature review with PROMISE repository dataset

Abstract

Software testing is an important and critical phase of software development life cycle to find software faults or defects and then correct those faults. However, testing process is a time-consuming activity that requires good planning and a lot of resources. Therefore, technique and methodology for predicting the testing effort is important process prior the testing process to significantly increase efficiency of time, effort and cost usage. Correspond to software metric usage for measuring software quality, software metric can be used to identify the faulty modules in software. Furthermore, implementing machine learning technique will allow computer to “learn” and able to predict the fault prone modules. Research in this field has become a hot issue for more than ten years ago. However, considering the high importance of software quality with support of machine learning methods development, this research area is still being highlighted until this year. In this paper, a survey of various software metric used for predicting software fault by using machine learning algorithm is examined. According to our review, this is the first study of software fault prediction that focuses to PROMISE repository dataset usage. Some conducted experiments from PROMISE repository dataset are compared to contribute a consensus on what constitute effective software metrics and machine learning method in software fault prediction.