Software defect prediction using call graph based ranking (CGBR) framework

Burak Turhan, Gozde Kocak, Ayse Bener

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

21 Citations (Scopus)

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 languageEnglish
Title of host publicationEUROMICRO 2008 - Proceedings of the 34th EUROMICRO Conference on Software Engineering and Advanced Applications, SEAA 2008
Pages191-198
Number of pages8
DOIs
Publication statusPublished - 1 Dec 2008
Externally publishedYes
EventEUROMICRO 2008 - Proceedings of the 34th EUROMICRO Conference on Software Engineering and Advanced Applications, SEAA 2008 - Parma, Italy
Duration: 3 Sep 20085 Sep 2008

Conference

ConferenceEUROMICRO 2008 - Proceedings of the 34th EUROMICRO Conference on Software Engineering and Advanced Applications, SEAA 2008
CountryItaly
CityParma
Period3/09/085/09/08

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