NDLI: Abstraction refinement guided by a learnt probabilistic model

Content Provider	ACM Digital Library
Author	Grigore, Radu Yang, Hongseok
Abstract	The 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).
Starting Page	485
Ending Page	498
Page Count	14
File Format	PDF
ISSN	03621340 15581160
DOI	10.1145/2914770.2837663
Journal	ACM SIGPLAN Notices (SIGP)
Volume Number	51
Issue Number	1
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	1983-05-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Hypergraph Horn Datalog Probability
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Software

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