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Data Visualization and Knowledge Engineering pp 1–21Cite as

Cross Projects Defect Prediction Modeling

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Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 32))

Abstract

Software defect prediction has been much studied in the field of research in Software Engineering. Within project Software defect prediction works well as there is sufficient amount of data available to train any model. But rarely local training data of the projects is available for predictions. There are many public defect data repositories available from various organizations. This availability leads to the motivation for Cross projects defect prediction. This chapter cites on defect prediction using cross projects defect data. We proposed two experiments with cross projects homogeneous metric set data and within projects data on open source software projects with class level information. The machine learning models including the ensemble approaches are used for prediction. The class imbalance problem is addressed using oversampling techniques. An empirical analysis is carried out to validate the performance of the models. The results indicate that cross projects defect prediction with homogeneous metric sets are comparable to within project defect prediction with statistical significance.

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Authors and Affiliations

  1. Ajay Kumar Garg Engineering College, Ghaziabad, India

    Lipika Goel & Sonam Gupta

Authors
  1. Lipika Goel
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  2. Sonam Gupta
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Correspondence to Sonam Gupta .

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Editors and Affiliations

  1. Department of Electronics and Communication Engineering (ECE), Karunya University, Coimbatore, Tamil Nadu, India

    Jude Hemanth

  2. Amity University, Noida, Uttar Pradesh, India

    Madhulika Bhatia

  3. Department of Electrical Engineering and Computer Science, Ştefan cel Mare University of Suceava, Suceava, Romania

    Oana Geman

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Goel, L., Gupta, S. (2020). Cross Projects Defect Prediction Modeling. In: Hemanth, J., Bhatia, M., Geman, O. (eds) Data Visualization and Knowledge Engineering. Lecture Notes on Data Engineering and Communications Technologies, vol 32. Springer, Cham. https://doi.org/10.1007/978-3-030-25797-2_1

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