Boosting Path-Sensitive Value Flow Analysis via Removal of Redundant Summaries

TL;DR

This paper introduces a novel method to identify and eliminate redundant summaries in path-sensitive value flow analysis, significantly improving scalability and efficiency without sacrificing accuracy.

Contribution

It presents the first approach to effectively remove redundant summaries in value flow analysis, reducing computational overhead and memory usage while maintaining soundness.

Findings

Reduced analysis time by up to 45% and memory by 27%.

Identified nearly 80% of redundant summaries efficiently.

Achieved an average performance gain of over 600 times in large programs.

Abstract

Value flow analysis that tracks the flow of values via data dependence is a widely used technique for detecting a broad spectrum of software bugs. However, the scalability issue often deteriorates when high precision (i.e., path-sensitivity) is required, as the instantiation of function summaries becomes excessively time- and memory-intensive. The primary culprit, as we observe, is the existence of redundant computations resulting from blindly computing summaries for a function, irrespective of whether they are related to bugs being checked. To address this problem, we present the first approach that can effectively identify and eliminate redundant summaries, thereby reducing the size of collected summaries from callee functions without compromising soundness or efficiency. Our evaluation on large programs demonstrates that our identification algorithm can significantly reduce the time and memory overhead of the state-of-the-art value flow analysis by 45\% and 27\%, respectively. Furthermore, the identification algorithm demonstrates remarkable efficiency by identifying nearly 80\% of redundant summaries while incurring a minimal additional overhead. In the largest \textit{mysqld} project, the identification algorithm reduces the time by 8107 seconds (2.25 hours) with a mere 17.31 seconds of additional overhead, leading to a ratio of time savings to paid overhead (i.e., performance gain) of 468.48 $\times$. In total, our method attains an average performance gain of 632.1 $\times$.