NDLI: An initial study on the use of execution complexity metrics as indicators of software vulnerabilities

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An initial study on the use of execution complexity metrics as indicators of software vulnerabilities

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An initial study on the use of execution complexity metrics as indicators of software vulnerabilities

Content Provider	ACM Digital Library
Author	Shin, Yonghee Williams, Laurie
Abstract	Allocating code inspection and testing resources to the most problematic code areas is important to reduce development time and cost. While complexity metrics collected statically from software artifacts are known to be helpful in finding vulnerable code locations, some complex code is rarely executed in practice and has less chance of its vulnerabilities being detected. To augment the use of static complexity metrics, this study examines execution complexity metrics that are collected during code execution as indicators of vulnerable code locations. We conducted case studies on two large size, widely-used open source projects, the Mozilla Firefox web browser and the Wireshark network protocol analyzer. Our results indicate that execution complexity metrics are better indicators of vulnerable code locations than the most commonly-used static complexity metric, lines of source code. The ability of execution complexity metrics to discriminate vulnerable code locations from neutral code locations and to predict vulnerable code locations vary depending on projects. However, the vulnerability prediction models using execution complexity metrics are superior to the models using static complexity metrics in reducing inspection effort.
Starting Page	1
Ending Page	7
Page Count	7
File Format	PDF
ISBN	9781450305815
DOI	10.1145/1988630.1988632
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-05-22
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Execution metrics Complexity metrics Software vulnerability prediction Software security
Content Type	Text
Resource Type	Article

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