A Mahalanobis distance based algorithm for assigning rank to the predicted fault prone software modules

Abstract

This article proposes a methodology based on Artificial Neural Network(ANN) and type-2 fuzzy logic system (FLS) for detecting the fault prone software modules at early development phase. The present research concentrates on software metrics from requirement analysis and design phase of software life cycle. A new approach has been developed to sort out degree of fault proneness (DFP) of the software modules through type-2 FLS. ANN is used to prepare the rule base for inference engine. Furthermore, the proposed model has induced an order relation among the fault prone modules (FPMs) with the help of Mahalanobis distance (MD) metric. During software development process, a project manager needs to recognize the fault prone software modules with their DFP. Hence, the present study is of great importance to the project personnel to develop more cost-effective and reliable software. KC2 dataset of NASA has been applied for validating the model. Performance analysis clearly indicates the better prediction capability of the proposed model compared to some existing similar models.