Abstraction Refinement Guided by a Learnt Probabilistic Model

TL;DR

This paper introduces a probabilistic-guided abstraction refinement method for static program analysis, improving the selection of program abstractions by learning from past analysis runs to optimize accuracy and efficiency.

Contribution

It presents a novel approach combining probabilistic models with counterexample-guided refinement for parametric static analyses in Datalog, with learned hyperparameters enhancing performance.

Findings

Effective abstraction selection improves analysis efficiency.

Probabilistic model accurately predicts success probabilities.

Method outperforms baseline approaches in Java pointer analysis.

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).