PARF: An Adaptive Abstraction-Strategy Tuner for Static Analysis

TL;DR

Parf is an automated toolkit that adaptively tunes abstraction strategies in static analyzers, improving accuracy and efficiency for analyzing complex C programs through probabilistic modeling and iterative refinement.

Contribution

It introduces a probabilistic, adaptive approach to tuning abstraction strategies in static analysis, implemented on top of Frama-C/Eva with a user-friendly interface.

Findings

Demonstrates competitive performance on large-scale real-world programs

Effectively identifies dominant parameters influencing analysis accuracy

Provides an intuitive web-based interface for configuration and visualization

Abstract

We launch Parf - a toolkit for adaptively tuning abstraction strategies of static program analyzers in a fully automated manner. Parf models various types of external parameters (encoding abstraction strategies) as random variables subject to probability distributions over latticed parameter spaces. It incrementally refines the probability distributions based on accumulated intermediate results generated by repeatedly sampling and analyzing, thereby ultimately yielding a set of highly accurate abstraction strategies. Parf is implemented on top of Frama-C/Eva - an off-the-shelf open-source static analyzer for C programs. Parf provides a web-based user interface facilitating the intuitive configuration of static analyzers and visualization of dynamic distribution refinement of the abstraction strategies. It further supports the identification of dominant parameters in Frama-C/Eva analysis. Benchmark experiments and a case study demonstrate the competitive performance of Parf for analyzing complex, large-scale real-world programs.