Research Article
An Improved Method for Cross-Project Defect Prediction by Simplifying Training Data
Table 2
Data statistics of the projects used in our experiments.
| Repository | Project | Version | #Instance | #Defect | % Defect |
| PROMISE | Ant | 1.7 | 745 | 166 | 22.3% | Camel | 1.6 | 965 | 188 | 19.5% | Ivy | 2.0 | 352 | 40 | 11.4% | Jedit | 3.2 | 272 | 90 | 33.1% | Lucene | 2.4 | 340 | 203 | 59.7% | Poi | 3.0 | 442 | 281 | 63.6% | Synapse | 1.2 | 256 | 86 | 33.6% | Velocity | 1.4 | 196 | 147 | 75.0% | Xalan | 2.6 | 885 | 411 | 46.4% | Xerces | 1.4 | 588 | 437 | 74.3% |
| AEEEM | Equinox | 1.1.2005–6.25.2008 | 324 | 129 | 39.8% | Eclipse JDT core (Eclipse) | 1.1.2005–6.17.2008 | 997 | 206 | 20.7% | Apache Lucene (Lucene2) | 1.1.2005–10.8.2008 | 692 | 20 | 2.9% | Mylyn | 1.17.2005–3.17.2009 | 1,862 | 245 | 13.2% | Eclipse PDE UI (Pde) | 1.1.2005–9.11.2008 | 1,497 | 209 | 14.0% |
|
|