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Calikli, Gul; Tosun, Ayse; Bener, Ayse and Celik, Melih
(2010).
DOI: https://doi.org/10.1109/ISCIS.2009.5291866
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.
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About
- Item ORO ID
- 45373
- Item Type
- Conference or Workshop Item
- ISBN
- 1-4244-5023-3, 978-1-4244-5023-7
- Keywords
- defect prediciton; Naïve Bayes Classifier; static code attributes; cost-benefit analysis
- Academic Unit or School
-
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM) - Related URLs
- Depositing User
- Gul Calikli