Software Defect Prediction Using Call Graph Based Ranking (CGBR) Framework

Software Defect Prediction Using Call Graph Based Ranking (CGBR) Framework

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. In this research we propose static call graph based ranking (CGBR) framework, which can be appl...

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Bibliographic Details
Published in:2008 34th Euromicro Conference Software Engineering and Advanced Applications pp. 191 - 198
Main Authors: Turhan, B., Kocak, G., Bener, A.
Format: Conference Proceeding
Language:English
Published: IEEE 01-09-2008
Subjects:
Application software
call graph
cost-benefit analysis
Costs
Data engineering
defect prediction
Humans
Predictive models
Software engineering
Software maintenance
Software measurement
Software quality
Software testing
Online Access:Get full text
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Summary: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. 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%.
ISBN:0769532764
9780769532769
ISSN:1089-6503
2376-9505
DOI:10.1109/SEAA.2008.52