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

Burak Turhan; Gozde Kocak; Ayse Bener

doi:10.1109/SEAA.2008.52

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

Burak Turhan, Gozde Kocak, Ayse Bener

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

26 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 language	English
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	https://doi.org/10.1109/SEAA.2008.52
Publication status	Published - 1 Dec 2008
Externally published	Yes
Event	Euromicro Conference on Software Engineering and Advanced Applications 2008 - Parma, Italy Duration: 3 Sep 2008 → 5 Sep 2008 Conference number: 34th https://dl.acm.org/doi/proceedings/10.5555/1494648 (Proceedings)

Conference

Conference	Euromicro Conference on Software Engineering and Advanced Applications 2008
Abbreviated title	SEAA 2008
Country/Territory	Italy
City	Parma
Period	3/09/08 → 5/09/08
Internet address	https://dl.acm.org/doi/proceedings/10.5555/1494648 (Proceedings)

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10.1109/SEAA.2008.52

Cite this

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title = "Software defect prediction using call graph based ranking (CGBR) framework",

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%.",

author = "Burak Turhan and Gozde Kocak and Ayse Bener",

year = "2008",

month = dec,

day = "1",

doi = "10.1109/SEAA.2008.52",

language = "English",

isbn = "9780769532769",

pages = "191--198",

booktitle = "EUROMICRO 2008 - Proceedings of the 34th EUROMICRO Conference on Software Engineering and Advanced Applications, SEAA 2008",

note = "Euromicro Conference on Software Engineering and Advanced Applications 2008, SEAA 2008 ; Conference date: 03-09-2008 Through 05-09-2008",

url = "https://dl.acm.org/doi/proceedings/10.5555/1494648",

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Turhan, B, Kocak, G & Bener, A 2008, Software defect prediction using call graph based ranking (CGBR) framework. in EUROMICRO 2008 - Proceedings of the 34th EUROMICRO Conference on Software Engineering and Advanced Applications, SEAA 2008., 4725722, pp. 191-198, Euromicro Conference on Software Engineering and Advanced Applications 2008, Parma, Italy, 3/09/08. https://doi.org/10.1109/SEAA.2008.52

Software defect prediction using call graph based ranking (CGBR) framework. / Turhan, Burak; Kocak, Gozde; Bener, Ayse.

EUROMICRO 2008 - Proceedings of the 34th EUROMICRO Conference on Software Engineering and Advanced Applications, SEAA 2008. 2008. p. 191-198 4725722.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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Y1 - 2008/12/1

N2 - 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%.

AB - 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%.

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

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