An empirical analysis of the statistical learning models for different categories of cross-project defect prediction
| Autor: | Lipika Goel, Mayank Sharma, D. Damodaran, Sunil Kumar Khatri |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
business.industry
Computer science Supervised learning General Engineering Statistical model Machine learning computer.software_genre Cross-validation Data modeling Computer Science Applications Empirical research Artificial intelligence Gradient boosting business computer Predictive modelling Software project management Software |
| Zdroj: | International Journal of Computer Aided Engineering and Technology. 14:233 |
| ISSN: | 1757-2665 1757-2657 |
| DOI: | 10.1504/ijcaet.2021.113549 |
| Popis: | Currently, the research community is addressing the problem of defect prediction with the availability of project defect data. The availability of different project data leads to extend the research on cross projects. Cross-project defect prediction has now become an accepted area of software project management. In this paper, an empirical study is carried out to investigate the predictive performance of availability within project and cross-project defect prediction models. Furthermore, different categories of cross-project data are taken for training and testing to analyse various statistical models. In this paper data models are analysed and compared using various statistical performance measures. The findings during the empirical analysis of the data models state that gradient boosting predictor outperforms in the cross-project defect prediction scenario. Results also infer that cross-project defect prediction is comparable with project defect prediction and has statistical significance. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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