Abstraction Refinement Guided by a Learnt Probabilistic Model

Cited 9 time in webofscience Cited 0 time in scopus
  • Hit : 346
  • Download : 0
DC FieldValueLanguage
dc.contributor.authorGrigore, Radu yko
dc.contributor.authorYang, Hongseokko
dc.date.accessioned2017-08-08T06:54:02Z-
dc.date.available2017-08-08T06:54:02Z-
dc.date.created2017-08-02-
dc.date.created2017-08-02-
dc.date.created2017-08-02-
dc.date.created2017-08-02-
dc.date.issued2016-01-
dc.identifier.citationACM SIGPLAN NOTICES, v.51, no.1, pp.485 - 498-
dc.identifier.issn0362-1340-
dc.identifier.urihttp://hdl.handle.net/10203/225265-
dc.description.abstractThe core challenge in designing an effective static program analysis is to find a good program abstraction - one that retains only details relevant to a given query. In this paper, we present a new approach for automatically finding such an abstraction. Our approach uses a pessimistic strategy, which can optionally use guidance from a probabilistic model. Our approach applies to parametric static analyses implemented in Datalog, and is based on counterexample-guided abstraction refinement. For each untried abstraction, our probabilistic model provides a probability of success, while the size of the abstraction provides an estimate of its cost in terms of analysis time. Combining these two metrics, probability and cost, our refinement algorithm picks an optimal abstraction. Our probabilistic model is a variant of the Erdos-Renyi random graph model, and it is tunable by what we call hyperparameters. We present a method to learn good values for these hyperparameters, by observing past runs of the analysis on an existing codebase. We evaluate our approach on an object sensitive pointer analysis for Java programs, with two client analyses (PolySite and Downcast).-
dc.languageEnglish-
dc.publisherASSOC COMPUTING MACHINERY-
dc.titleAbstraction Refinement Guided by a Learnt Probabilistic Model-
dc.typeArticle-
dc.identifier.wosid000374053600039-
dc.identifier.scopusid2-s2.0-84965064229-
dc.type.rimsART-
dc.citation.volume51-
dc.citation.issue1-
dc.citation.beginningpage485-
dc.citation.endingpage498-
dc.citation.publicationnameACM SIGPLAN NOTICES-
dc.identifier.doi10.1145/2837614.2837663-
dc.contributor.localauthorYang, Hongseok-
dc.contributor.nonIdAuthorGrigore, Radu y-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle; Proceedings Paper-
dc.subject.keywordAuthorDatalog-
dc.subject.keywordAuthorHorn-
dc.subject.keywordAuthorhypergraph-
dc.subject.keywordAuthorprobability-
Appears in Collection
CS-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 9 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0