Finding Optimum Abstractions in Parametric Dataflow Analysis

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dc.contributor.authorZhang, Xinko
dc.contributor.authorNaik, Mayurko
dc.contributor.authorYang, Hongseokko
dc.date.accessioned2017-08-08T06:54:14Z-
dc.date.available2017-08-08T06:54:14Z-
dc.date.created2017-08-02-
dc.date.created2017-08-02-
dc.date.created2017-08-02-
dc.date.created2017-08-02-
dc.date.issued2013-06-
dc.identifier.citationACM SIGPLAN NOTICES, v.48, no.6, pp.365 - 376-
dc.identifier.issn0362-1340-
dc.identifier.urihttp://hdl.handle.net/10203/225274-
dc.description.abstractWe propose a technique to efficiently search a large family of abstractions in order to prove a query using a parametric dataflow analysis. Our technique either finds the cheapest such abstraction or shows that none exists. It is based on counterexample-guided abstraction refinement but applies a novel meta-analysis on abstract counterexample traces to efficiently find abstractions that are incapable of proving the query. We formalize the technique in a generic framework and apply it to two analyses: a type-state analysis and a thread-escape analysis. We demonstrate the effectiveness of the technique on a suite of Java benchmark programs.-
dc.languageEnglish-
dc.publisherASSOC COMPUTING MACHINERY-
dc.titleFinding Optimum Abstractions in Parametric Dataflow Analysis-
dc.typeArticle-
dc.identifier.wosid000321865400035-
dc.identifier.scopusid2-s2.0-84880111118-
dc.type.rimsART-
dc.citation.volume48-
dc.citation.issue6-
dc.citation.beginningpage365-
dc.citation.endingpage376-
dc.citation.publicationnameACM SIGPLAN NOTICES-
dc.identifier.doi10.1145/2499370.2462185-
dc.contributor.localauthorYang, Hongseok-
dc.contributor.nonIdAuthorZhang, Xin-
dc.contributor.nonIdAuthorNaik, Mayur-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle; Proceedings Paper-
dc.subject.keywordAuthorDataflow analysis-
dc.subject.keywordAuthorCEGAR-
dc.subject.keywordAuthorabstraction refinement-
dc.subject.keywordAuthoroptimum abstraction-
dc.subject.keywordAuthorimpossibility-
dc.subject.keywordAuthorunder-approximation-
dc.subject.keywordAuthorLanguages-
dc.subject.keywordAuthorVerification-
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