Cross-project defect prediction using a credibility theory based naive bayes classifier
Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45) › 32_Refereed conference paper (with host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Title of host publication | 2017 IEEE International Conference on Software Quality, Reliability and Security (QRS) |
| Publisher | IEEE Computer Society |
| Pages | 434-441 |
| ISBN (Print) | 978-1-5386-0592-9 |
| Publication status | Published - Jul 2017 |
Conference
| Title | 17th IEEE International Conference on Software Quality, Reliability and Security, QRS 2017 |
|---|---|
| Place | Czech Republic |
| City | Prague |
| Period | 25 - 29 July 2017 |
Link(s)
Abstract
Several defect prediction models proposed are effective when historical datasets are available. Defect prediction becomes difficult when no historical data exist.
Cross-project defect prediction (CPDP), which uses projects from other sources/companies to predict the defects in the target projects proposed in recent studies has shown promising results. However, the performance of most CPDP approaches are still beyond satisfactory mainly due to distribution
mismatch between the source and target projects. In this study, a credibility theory based Naïve Bayes (CNB) classifier is proposed to establish a novel reweighting mechanism between the source projects and target projects so that the source data could simultaneously adapt to the target data distribution and
retain its own pattern. Our experimental results show that the feasibility of the novel algorithm design and demonstrate the significant improvement in terms of the performance metrics considered achieved by CNB over other CPDP approaches.
Cross-project defect prediction (CPDP), which uses projects from other sources/companies to predict the defects in the target projects proposed in recent studies has shown promising results. However, the performance of most CPDP approaches are still beyond satisfactory mainly due to distribution
mismatch between the source and target projects. In this study, a credibility theory based Naïve Bayes (CNB) classifier is proposed to establish a novel reweighting mechanism between the source projects and target projects so that the source data could simultaneously adapt to the target data distribution and
retain its own pattern. Our experimental results show that the feasibility of the novel algorithm design and demonstrate the significant improvement in terms of the performance metrics considered achieved by CNB over other CPDP approaches.
Research Area(s)
- Credibility theory, Cross-project defect prediction, Naive Bayes classifier, Quality assurance, Software engineering, Transfer learning
Citation Format(s)
Cross-project defect prediction using a credibility theory based naive bayes classifier. / Poon, Wai Nam; Bennin, Kwabena Ebo; Huang, Jianglin et al.
2017 IEEE International Conference on Software Quality, Reliability and Security (QRS) . IEEE Computer Society, 2017. p. 434-441 8009947.
2017 IEEE International Conference on Software Quality, Reliability and Security (QRS) . IEEE Computer Society, 2017. p. 434-441 8009947.
Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45) › 32_Refereed conference paper (with host publication) › peer-review
