Software fault prediction and classification using cost based random forest in spiral life cycle model

Abstract

In the domain of software engineering many new techniques are deployed for identifying the fault in software modules. This part of software design plays a fundamental role cause of its assurance towards higher reliability and stability. Many existing techniques like Bayesian approach have been employed to minimize the software faults but they can't able to predict efficiently within limited resources. In this paper, a new classification and prediction methodology is put forth to progress the accuracy of defect forecast based on Cost Random Forest algorithm (CRF) which reduces the effects of faults in irrelevant software modules. The proposed algorithm predicts the quantity of faults present in the modules of software in less time and classify based on measures of similarity obtained from Robust Similarity clustering technique. The overall results inferred from this methodology proven that this CRF can be capable to rank the module's faults in order to enhance the software development quality.