An Empirical Analysis of Software Fault Proneness Using Factor Analysis with Regression
Abstract During the early stages of the life cycle development process for software, the developer mainly makes use of the fault prediction process for the development of different modules. These modules help in detecting faulty modules and classes. Further, this process also helps in determining the modules which require a high level of refactoring during the maintenance stage. The objective of this research is to classify faults and to explore the usability of Factor Analysis with Regression (FAWR) which drastically ameliorate the system performance. A review of recent studies performed that uses the different fault prediction techniques. To direct this research, two research questions (RQ) are defined, one related to the integration of techniques to enhance the development of fault prediction model, and another is to check the technique to overcome the limitations of old methods. To answer these RQs, FAWR techniques are used for predicting faults. To assess the quality of the technique, two experiments were conducted. Results show that FAWR is the better performing method among the two prediction methods investigated. The results proved that the prediction capability of FAWR technique is significantly better. Factorization method is able to classify a module whether it is fault-prone or not. The constructed models use to estimate the proneness of faults surpass the standard regression models. The system evaluations indicate that the reduction of terms results in the betterment of outcomes. Moreover, the consideration of FAWR is a significant technique for the prediction of faults in software.