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[1711.03239] Active Learning of Points-To Specifications
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[v1] Thu, 9 Nov 2017 03:03:35 UTC (131 KB)
[v2] Fri, 17 Nov 2017 18:16:05 UTC (147 KB)
[v3] Tue, 22 May 2018 01:27:47 UTC (150 KB)
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Computer Science > Programming Languages
arXiv:1711.03239 (cs)
[Submitted on 9 Nov 2017 (v1), last revised 22 May 2018 (this version, v3)]
Title:Active Learning of Points-To Specifications
View a PDF of the paper titled Active Learning of Points-To Specifications, by Osbert Bastani and 3 other authors
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Abstract:When analyzing programs, large libraries pose significant challenges to static points-to analysis. A popular solution is to have a human analyst provide points-to specifications that summarize relevant behaviors of library code, which can substantially improve precision and handle missing code such as native code. We propose ATLAS, a tool that automatically infers points-to specifications. ATLAS synthesizes unit tests that exercise the library code, and then infers points-to specifications based on observations from these executions. ATLAS automatically infers specifications for the Java standard library, and produces better results for a client static information flow analysis on a benchmark of 46 Android apps compared to using existing handwritten specifications.
| Subjects: | Programming Languages (cs.PL) |
| Cite as: | arXiv:1711.03239 [cs.PL] |
| (or arXiv:1711.03239v3 [cs.PL] for this version) | |
| https://doi.org/10.48550/arXiv.1711.03239
arXiv-issued DOI via DataCite
|
Submission history
From: Osbert Bastani [view email][v1] Thu, 9 Nov 2017 03:03:35 UTC (131 KB)
[v2] Fri, 17 Nov 2017 18:16:05 UTC (147 KB)
[v3] Tue, 22 May 2018 01:27:47 UTC (150 KB)
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View a PDF of the paper titled Active Learning of Points-To Specifications, by Osbert Bastani and 3 other authors
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