Selective Context-Sensitivity Guided by Impact Pre-Analysis

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We present a method for selectively applying context-sensitivity during interprocedural program analysis. Our method applies context-sensitivity only when and where doing so is likely to improve the precision that matters for resolving given queries. The idea is to use a pre-analysis to estimate the impact of context-sensitivity on the main analysis's precision, and to use this information to find out when and where the main analysis should turn on or off its context-sensitivity. We formalize this approach and prove that the analysis always benefits from the pre-analysis-guided context-sensitivity. We implemented this selective method for an existing industrial-strength interval analyzer for full C. The method reduced the number of (false) alarms by 24.4%, while increasing the analysis cost by 27.8% on average. The use of the selective method is not limited to context-sensitivity. We demonstrate this generality by following the same principle and developing a selective relational analysis.
Publisher
ASSOC COMPUTING MACHINERY
Issue Date
2014-06
Language
English
Article Type
Article; Proceedings Paper
Citation

ACM SIGPLAN NOTICES, v.49, no.6, pp.475 - 484

ISSN
0362-1340
DOI
10.1145/2594291.2594318
URI
http://hdl.handle.net/10203/225271
Appears in Collection
CS-Journal Papers(저널논문)
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