Abstract
In this paper, we present a defect prediction model based on ensemble of classifiers, which has not been fully explored so far in this type of research. We have conducted several experiments on public datasets. Our results reveal that ensemble of classifiers considerably improve the defect detection capability compared to Naive Bayes algorithm. We also conduct a cost-benefit analysis for our ensemble, where it turns out that it is enough to inspect 32% of the code on the average, for detecting 76% of the defects.
| Original language | English |
|---|---|
| Title of host publication | ESEM'08 |
| Subtitle of host publication | Proceedings of the 2008 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement |
| Pages | 318-320 |
| Number of pages | 3 |
| DOIs | |
| Publication status | Published - 1 Dec 2008 |
| Externally published | Yes |
| Event | 2nd International Symposium on Empirical Software Engineering and Measurement, ESEM 2008 - Kaiserslautern, Germany Duration: 9 Oct 2008 → 10 Oct 2008 |
Conference
| Conference | 2nd International Symposium on Empirical Software Engineering and Measurement, ESEM 2008 |
|---|---|
| Country | Germany |
| City | Kaiserslautern |
| Period | 9/10/08 → 10/10/08 |
Keywords
- Defect prediction
- Ensemble of classifiers
- Static code attributes
Cite this
Tosun, A., Turhan, B., & Bener, A. (2008). Ensemble of software defect predictors: A case study. In ESEM'08: Proceedings of the 2008 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement (pp. 318-320) https://doi.org/10.1145/1414004.1414066