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DC Field | Value | Language |
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dc.contributor.author | Hosseini, R | - |
dc.contributor.author | Turhan, B | - |
dc.contributor.author | Gunarathna, D | - |
dc.date.accessioned | 2017-11-01T13:50:14Z | - |
dc.date.available | 2017-11-01T13:50:14Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | IEEE Transactions on Software Engineering, (2017) | en_US |
dc.identifier.issn | 0098-5589 | - |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/15341 | - |
dc.description.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. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.subject | Defect Prediction | en_US |
dc.subject | Fault Prediction | en_US |
dc.subject | Cross Project | en_US |
dc.subject | Systematic Literature Review | en_US |
dc.subject | Meta-analysis | en_US |
dc.subject | Within Project | en_US |
dc.title | A Systematic Literature Review and Meta-analysis on Cross Project Defect Prediction | en_US |
dc.type | Article | en_US |
dc.relation.isPartOf | IEEE Transactions on Software Engineering | - |
pubs.publication-status | Accepted | - |
Appears in Collections: | Dept of Computer Science Research Papers |
Files in This Item:
File | Description | Size | Format | |
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Fulltext.pdf | 1.58 MB | Adobe PDF | View/Open |
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