Name
Abstract | ||
|---|---|---|
Application of defect predictors in software development helps the managers to allocate their resources such as time and effort more efficiently and cost effectively to test certain sections of the code. In this research, we have used Naive Bayes Classifier (NBC) to construct our defect prediction framework. Our proposed framework uses the hierarchical structure information about the source code of the software product, to perform defect prediction at a functional method level and source file level. We have applied our model on SoftLAB and Eclipse datasets. We have measured the performance of our proposed model and applied cost benefit analysis. Our results reveal that source file level defect prediction improves the verification effort, while decreasing the defect prediction performance in all datasets. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ISCIS.2009.5291866 | ISCIS |
Keywords | Field | DocType |
pattern classification,program testing,program verification,Eclipse dataset,SoftLAB dataset,cost benefit analysis,functional method level,granularity level,naive Bayes classifier,software defect prediction,software development,source file level,static code attributes,Naïve Bayes Classifier,component,cost-benefit analysis,defect prediciton,static code attributes | Data mining,Data modeling,Naive Bayes classifier,Source code,Computer science,Software bug,Software,Eclipse,Artificial intelligence,Granularity,Software development,Machine learning | Conference |
Citations | PageRank | References |
5 | 0.42 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gul Calikli | 1 | 61 | 9.41 |
| Ayse Tosun | 2 | 140 | 13.20 |
| Bener, Ayse Basar | 3 | 137 | 18.85 |
| Melih Celik | 4 | 5 | 0.42 |