Incremental and Modular Context-sensitive Analysis

TL;DR

This paper introduces an efficient incremental, context-sensitive analysis algorithm for modular programs, significantly reducing time and memory costs compared to existing methods, and outperforming traditional analysis even when starting from scratch.

Contribution

It presents a novel modular incremental analysis algorithm that leverages program structure for improved efficiency and effectiveness over prior fine-grained and non-modular techniques.

Findings

Significant improvements in time and memory consumption.

Outperforms existing non-modular incremental analysis.

Outperforms traditional modular analysis from scratch.

Abstract

Context-sensitive global analysis of large code bases can be expensive, which can make its use impractical during software development. However, there are many situations in which modifications are small and isolated within a few components, and it is desirable to reuse as much as possible previous analysis results. This has been achieved to date through incremental global analysis fixpoint algorithms that achieve cost reductions at fine levels of granularity, such as changes in program lines. However, these fine-grained techniques are not directly applicable to modular programs, nor are they designed to take advantage of modular structures. This paper describes, implements, and evaluates an algorithm that performs efficient context-sensitive analysis incrementally on modular partitions of programs. The experimental results show that the proposed modular algorithm shows significant improvements, in both time and memory consumption, when compared to existing non-modular, fine-grain incremental analysis techniques. Furthermore, thanks to the proposed inter-modular propagation of analysis information, our algorithm also outperforms traditional modular analysis even when analyzing from scratch.