Defect prediction model of static code features for cross-company and cross-project software

Abstract

Software project metrics are seen needless in software industries but they are useful when some unacceptable situations come in the project (Satapathy et al., Proceedings of the 48th annual convention of CSI, vol 2, 2013). Mainly the focus of various defect prediction studies is to build prediction models using the regional data available within the company. So companies maintain a data repository where data of their past projects can be stored which can be used for defect prediction in the future. However, many companies do not follow this practice. In software engineering, the crucial task is Defect prediction. In this paper, a binary defect prediction model was built and examined if there is any conclusion or not. This paper presents the assets of cross-company and within-company data against software defect prediction. Neural network approach has been used to prepare the model for defect prediction. Further, this paper compares the results of with-in and cross-company defect prediction models. To analyse the results for with-in company two versions of Firefox (i.e. 2.0 and 3.0) were considered; for cross project one version of Mozila Sea Monkey (1.0.1); for cross-company validation one version of LICQ were considered. Main focus of the study is to analyse the behavior or role of software metrics for acceptable level of defect prediction.