Abstract
Software Defect Prediction (SDP) is one of the key tasks in the testing phase of Software Development Life Cycle (SDLC) that discovers modules that are more susceptible to defects and therefore requires significant testing to identify these flaws early in order to cut up the extra cost for software development. Much research has been performed on Cross-Project Defect Prediction (CPDP), which seeks to predict defects in the target application that lacks historical defect prediction information or has restricted defect information to construct an efficient generalized model for forecasting defects in a software project. The proposed research work focuses on defect forecast using a heterogeneous metric set so that there are no common metrics between the source and the target applications. This paper also discusses the Class Imbalance Problem (CIP) that occurs in a dataset because of the disproportionate number of favorable and unfavorable cases. If trained using imbalance dataset, a classifier will offer biased outcomes. We used Adaptive Boost (AdaBoost) method to manage CIP in Heterogeneous Cross-Project Defect Prediction (HCPDP), and after managing CIP, experimental findings demonstrate significant improvements.
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Vashisht, R., Rizvi, S.A.M. (2021). Handling Class Imbalance Problem in Heterogeneous Cross-Project Defect Prediction. In: Gupta, D., Khanna, A., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1165. Springer, Singapore. https://doi.org/10.1007/978-981-15-5113-0_7
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