![]() The experimental results indicate that WIFLF significantly outperforms all the baselines. Besides, WIFLF is compared with 12 approaches. Four groups of datasets from AEEEM, Relink and PROMISE Data Repository are used to conduct CPDP models. This paper presents a Weighted Isolation Forest with class Label information Filter (WIFLF) to relieve the issues above. Therefore, it is vital to explore a target project-agnostic approach to build CPDP models. Additionally, when there is a large amount of source data that needs to be matched with target data, it will take much time and reduce the efficiency of model construction. That is, the performance of these methods is determined by the effectiveness of feature transformation or the similarity between two projects. However, existing techniques are strongly dependent on the target data to reduce the distribution discrepancy between source and target projects. There has been several approaches to improve the performance of CPDP, such as feature transformation and instance selection methods. Cross-project defect prediction (CPDP) is used to build defect prediction models when data from the target project are not enough.
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