Abstraction Refinement Guided by a Learnt Probabilistic Model

Grigore, Radu (2016) Abstraction Refinement Guided by a Learnt Probabilistic Model. In: Principles of Programming Languages, 2016, New York, USA. (doi:https://doi.org/10.1145/2837614.2837663) (Full text available)

PDF (with additional proofs (in appendix)) - Publisher pdf
Download (475kB) Preview
Official URL


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).

Item Type: Conference or workshop item (Paper)
Uncontrolled keywords: program analysis, datalog, machine learning, erdos-renyi
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.76 Computer software
Divisions: Faculties > Sciences > School of Computing > Programming Languages and Systems Group
Depositing User: Radu Grigore
Date Deposited: 09 Feb 2016 15:46 UTC
Last Modified: 24 Nov 2016 18:01 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/54057 (The current URI for this page, for reference purposes)
  • Depositors only (login required):


Downloads per month over past year