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http://bura.brunel.ac.uk/handle/2438/15341
Title: | A Systematic Literature Review and Meta-analysis on Cross Project Defect Prediction |
Authors: | Hosseini, R Turhan, B Gunarathna, D |
Keywords: | Defect Prediction;Fault Prediction;Cross Project;Systematic Literature Review;Meta-analysis;Within Project |
Issue Date: | 2017 |
Publisher: | IEEE |
Citation: | IEEE Transactions on Software Engineering, (2017) |
Abstract: | Background: Cross project defect prediction (CPDP) recently gained considerable attention, yet there are no systematic efforts to analyse existing empirical evidence. Objective: To synthesise literature to understand the state-of-the-art in CPDP with respect to metrics, models, data approaches, datasets and associated performances. Further, we aim to assess the performance of CPDP vs. within project DP models. Method: We conducted a systematic literature review. Results from primary studies are synthesised (thematic, meta-analysis) to answer research questions. Results: We identified 30 primary studies passing quality assessment. Performance measures, except precision, vary with the choice of metrics. Recall, precision, f-measure, and AUC are the most common measures. Models based on Nearest-Neighbour and Decision Tree tend to perform well in CPDP, whereas the popular na¨ıve Bayes yield average performance. Performance of ensembles varies greatly across f-measure and AUC. Data approaches address CPDP challenges using row/column processing, which improve CPDP in terms of recall at the cost of precision. This is observed in multiple occasions including the meta-analysis of CPDP vs. WPDP. NASA and Jureczko datasets seem to favour CPDP over WPDP more frequently. Conclusion: CPDP is still a challenge and requires more research before trustworthy applications can take place. We provide guidelines for further research. |
URI: | http://bura.brunel.ac.uk/handle/2438/15341 |
ISSN: | 0098-5589 |
Appears in Collections: | Dept of Computer Science Research Papers |
Files in This Item:
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Fulltext.pdf | 1.58 MB | Adobe PDF | View/Open |
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