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Defect prediction model using transfer learning
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DOI：10.1007/s00500-022-06846-x.png)
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Abstract
En 中文
Software defect prediction (SDP) plays an important role in new research areas of software engineering. Cross-project defect prediction (CPDP) technique achieved success for prediction of defects in innovating projects having lack of data. In this study, we have developed predictive models using different machine learning algorithms. Two approaches for DP were discussed in this study. One approach is heterogeneous defect prediction (HDP), and another approach is within-project defect prediction (WPDP). We have used different projects in HDP. The metrics matching analyzer is used for identifying matching metrics across different projects. The concept of transfer learning helps to improve DP by using HDP. HDP takes one project for training and testing against another project. We have shown the best machine learning algorithm for the DP model. The DP helps us in identifying defects before its delivery to the customer. To achieve our goal, we have taken twelve datasets from NASA and PROMISE repository. The machine learning algorithms are compared using statistical tests. The performance of the developed prediction model has been evaluated using area under curve performance measure
Keywords:
Software defect prediction
Transfer learning
Heterogeneous defect prediction
Machine learning
Statistical test