Abstract
Recent research on static code attribute (SCA) based defect prediction suggests that a performance ceiling has been achieved and this barrier can be exceeded by increasing the information content in data [18]. In this research we propose static call graph based ranking (CGBR) framework, which can be applied to any defect prediction model based on SCA. In this framework, we model both intra module properties and inter module relations. Our results show that defect predictors using CGBR framework can detect the same number of defective modules, while yielding significantly lower false alarm rates. On industrial public data, we also show that using CGBR framework can improve testing efforts by 23%.
Original language | English |
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Title of host publication | EUROMICRO 2008 - Proceedings of the 34th EUROMICRO Conference on Software Engineering and Advanced Applications, SEAA 2008 |
Pages | 191-198 |
Number of pages | 8 |
DOIs | |
Publication status | Published - 1 Dec 2008 |
Externally published | Yes |
Event | EUROMICRO 2008 - Proceedings of the 34th EUROMICRO Conference on Software Engineering and Advanced Applications, SEAA 2008 - Parma, Italy Duration: 3 Sep 2008 → 5 Sep 2008 |
Conference
Conference | EUROMICRO 2008 - Proceedings of the 34th EUROMICRO Conference on Software Engineering and Advanced Applications, SEAA 2008 |
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Country | Italy |
City | Parma |
Period | 3/09/08 → 5/09/08 |