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Machine-Learning-Guided Selectively Unsound Static Analysis
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Heo, K. | - |
dc.contributor.author | Oh, H. | - |
dc.contributor.author | Yi, K. | - |
dc.date.accessioned | 2023-06-27T06:38:19Z | - |
dc.date.available | 2023-06-27T06:38:19Z | - |
dc.date.created | 2023-06-19 | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | Proceedings - 2017 IEEE/ACM 39th International Conference on Software Engineering, ICSE 2017, pp.519-529 | - |
dc.identifier.uri | https://hdl.handle.net/10371/192904 | - |
dc.description.abstract | We present a machine-learning-based technique for selectively applying unsoundness in static analysis. Existing bug-finding static analyzers are unsound in order to be precise and scalable in practice. However, they are uniformly unsound and hence at the risk of missing a large amount of real bugs. By being sound, we can improve the detectability of the analyzer but it often suffers from a large number of false alarms. Our approach aims to strike a balance between these two approaches by selectively allowing unsoundness only when it is likely to reduce false alarms, while retaining true alarms. We use an anomaly-detection technique to learn such harmless unsoundness. We implemented our technique in two static analyzers for full C. One is for a taint analysis for detecting format-string vulnerabilities, and the other is for an interval analysis for buffer-overflow detection. The experimental results show that our approach significantly improves the recall of the original unsound analysis without sacrificing the precision. © 2017 IEEE. | - |
dc.language | 영어 | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Machine-Learning-Guided Selectively Unsound Static Analysis | - |
dc.type | Article | - |
dc.citation.journaltitle | Proceedings - 2017 IEEE/ACM 39th International Conference on Software Engineering, ICSE 2017 | - |
dc.identifier.scopusid | 2-s2.0-85027716023 | - |
dc.citation.endpage | 529 | - |
dc.citation.startpage | 519 | - |
dc.description.isOpenAccess | N | - |
dc.contributor.affiliatedAuthor | Yi, K. | - |
dc.description.journalClass | 1 | - |
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