An Incremental Algorithm for Algebraic Program Analysis

TL;DR

This paper introduces an incremental algebraic program analysis method that efficiently updates analysis results after program changes, significantly speeding up the process compared to traditional approaches.

Contribution

It presents a novel tree-based data structure and techniques for incremental updating of program properties, improving analysis efficiency after program modifications.

Findings

Speedup ranging from 160X to 4761X over baseline methods

Effective in analyzing Java applications from DaCapo suite

Significantly reduces analysis time after program changes

Abstract

We propose a method for conducting algebraic program analysis (APA) incrementally in response to changes of the program under analysis. APA is a program analysis paradigm that consists of two distinct steps: computing a path expression that succinctly summarizes the set of program paths of interest, and interpreting the path expression using a properly-defined semantic algebra to obtain program properties of interest. In this context, the goal of an incremental algorithm is to reduce the analysis time by leveraging the intermediate results computed before the program changes. We have made two main contributions. First, we propose a data structure for efficiently representing path expression as a tree together with a tree-based interpreting method. Second, we propose techniques for efficiently updating the program properties in response to changes of the path expression. We have implemented our method and evaluated it on thirteen Java applications from the DaCapo benchmark suite. The experimental results show that both our method for incrementally computing path expression and our method for incrementally interpreting path expression are effective in speeding up the analysis. Compared to the baseline APA and two state-of-the-art APA methods, the speedup of our method ranges from 160X to 4761X depending on the types of program analyses performed.