Abstract

Today, defect prediction is an important part of software industry to meet deadlines for their products. Defect prediction techniques help the organizations to use their resources effectively which results in lower cost and time requirements. Various metrics are used for defect prediction in within company (WC) and cross-company (CC) projects. In this paper, we used object-oriented metrics to build a defect prediction model for within company and cross-company projects. In this paper, feed-forward neural network (FFNN) model is used to build a defect prediction model. The proposed model was tested over four datasets against within company defect prediction (WCDP) and cross-company defect prediction (CCDP). The proposed model gives good results for WCDP and CCDP as compared to previous studies.

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