An Effective Defect Detection and Warning Prioritization Approach for Resource Leaks

Failing to release unneeded system resources such as I/O streams can result in resource leaks, which can lead to performance degradation and system crashes. Existing resource-leak detectors are usually based on predefined defect patterns to detect resource leaks in software. However, they typically...

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Bibliographic Details
Published in2012 IEEE 36th Annual Computer Software and Applications Conference pp. 119 - 128
Main Authors Liang, Guangtai, Wu, Qian, Wang, Qianxiang, Mei, Hong
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2012
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ISBN9781467319904
1467319902
ISSN0730-3157
DOI10.1109/COMPSAC.2012.22

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Summary:Failing to release unneeded system resources such as I/O streams can result in resource leaks, which can lead to performance degradation and system crashes. Existing resource-leak detectors are usually based on predefined defect patterns to detect resource leaks in software. However, they typically report too many false positives and negatives, and also lack effective warning prioritization. Our empirical investigation shows that, their predefined defect patterns are not precise enough, and moreover, their used defect detection processes are not suitable enough for the defect patterns. In our approach, we introduce a novel Expressive Defect Pattern Specification Notation (EDPSN). With EDPSN, a resource-leak defect pattern can be defined more precisely by specifying conditional method calls and more expressively by including guiding information for the defect detection and warning prioritization process, such as the characteristics of its preferred defect detection process and the effective prioritization impact factors for its related warnings. Based on the EDPSN-based defect pattern, our approach tries to flexibly tune out a suitable defect detection and warning prioritization process. Through evaluations on three real-world projects (Eclipse-3.0.1, JBoss-3.0.6, and Weka-3.6.4), we show that our approach achieves high average precision (96%) and recall (74%), 26% and 49% higher than existing approaches, respectively.
ISBN:9781467319904
1467319902
ISSN:0730-3157
DOI:10.1109/COMPSAC.2012.22