Automatically Tailoring Static Analysis to Custom Usage Scenarios

TL;DR

This paper introduces TAILOR, a framework that automatically customizes static analyzers, specifically abstract interpreters, to specific code bases and resource constraints, significantly improving analysis effectiveness over default settings.

Contribution

The paper presents a novel automated technique for tailoring static analyzers to specific code and resource scenarios, enhancing their practical utility.

Findings

TAILOR-generated configurations outperform default options

Configurations vary significantly across different code bases

Tailored configurations remain effective across code versions

Abstract

In recent years, there has been significant progress in the development and industrial adoption of static analyzers. Such analyzers typically provide a large, if not huge, number of configurable options controlling the precision and performance of the analysis. A major hurdle in integrating static analyzers in the software-development life cycle is tuning their options to custom usage scenarios, such as a particular code base or certain resource constraints. In this paper, we propose a technique that automatically tailors a static analyzer, specifically an abstract interpreter, to the code under analysis and any given resource constraints. We implement this technique in a framework called TAILOR, which we use to perform an extensive evaluation on real-world benchmarks. Our experiments show that the configurations generated by TAILOR are vastly better than the default analysis options, vary significantly depending on the code under analysis, and most remain tailored to several subsequent code versions.