Description
Title: A NEW CLUSTERING METHOD FOR SELECTING TRAINING INSTANCES OF CROSS-PROJECT DEFECT PREDICTION IS COMPLEXFUZZY.
Abstract: Researchers have been examining whether cross-project defect prediction (CPDP) offers advantages over conventional defect prediction settings for the past ten years. These works use different projects instead of the same project’s training and testing data for defect prediction. Choosing the appropriate training data is crucial to the success of CPDP. This study presents complexFuzzy, a novel clustering technique for choosing the CPDP training data. The technique identifies the most flawed examples, which the experimental predictors use to finish the training. In order to achieve this, a fuzzy-based membership is built using the data sets. Thus, overfitting is lessened, which is a major issue with CPDP training. Using 4 classifiers, the performance of complex fuzzy is compared to its 5 counterparts on 29 data sets. The results show that complex Fuzzy performs better in CPDP than other clustering techniques.
Keywords: cross-project defect prediction, complexFuzzy, training instance selection, fuzzy clustering
Paper Quality: SCOPUS / Web of Science Level Research Paper
Paper type: Analysis Based Research Paper
Subject: Computer Science
Writer Experience: 20+ Years
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