Modeling Structural Model for Defect Categories Based On Software Metrics for Categorical Defect Prediction

Abstract

Software Defect prediction is the pre-eminent area of software engineering which has witnessed huge importance over last decades. The identification of defects in the early stages of software development not only improve the quality of the software system but also reduce the time, cost and effort associated in maintaining the quality of software product. The quality of the software can be best assessed by software metrics. To evaluate the quality of the software, a number of software metrics have been proposed. Many research studies have been conducted to construct the prediction model that considers the CK (Chidamber and Kemerer) metrics suite and object oriented software metrics. For the prediction model development, consideration of interaction among the metrics is not a common practice. This paper presents the empirical evaluation in which several software metrics were investigated in order to identify the effective set of the metrics for each defect category which can significantly improve the defect prediction model made for each defect category. For each of the metrics, Pearson correlation coefficient with the number of defect categories were calculated and subsequently stepwise regression model is constructed for each defect category to predict the set of the metrics that are the good indicator of each defect category. We have proposed a novel approach for modelling the defects using structural equation modeling further which validates our work. Structural models were built for each defect category using structural equation modeling which claims that results are validated.