一句话：构建真实程序变更基准与统一评估框架，实证揭示增量程序分析工具的效率优势与内存、一致性缺陷。
增量程序分析（IPA）利用已有分析结果应对程序变更，以维持分析结果的时效性，近年来涌现出多种算法与工具，但其在真实场景下的实证表现仍缺乏深入研究。本文指出当前评估存在两个关键局限：缺乏反映真实程序变更的标准化基准，以及评估指标不足且分布不均衡。为此，作者提出自动构建真实程序变更基准的流水线，并开发统一的增量评估框架，以系统评估 IPA 工具。基于该流水线，他们从 20 个 Java 项目的 4,084 次 commit 构建大规模基准，并系统评估了两个面向 Java 的 IPA 工具。结果表明：增量分析相比全量分析显著提升效率，但现有 IPA 工具表现出不一致性，且峰值内存消耗明显更高。最后，作者提炼了若干实践洞见，以指导增量程序分析领域的未来研究与开发。
Incremental program analysis (IPA) has gained increasing attention as an effective approach for maintaining up-to-date analysis results by leveraging previously computed results in response to program changes. Consequently, a variety of IPA algorithms and tools have been proposed. However, their empirical performance in practical, real-world scenarios remains insufficiently investigated. To address this gap, this study presents a comprehensive examination of the current state-of-the-art in IPA evaluation. Specifically, we identify two key limitations: (1) the lack of standardized benchmarks reflecting real-world program changes, and (2) the inadequacy and imbalanced distribution of evaluation metrics.To overcome these challenges, we propose an automated pipeline for constructing real-world program change benchmarks and develop a unified incremental evaluation framework for systematically evaluating IPA tools. Using the proposed evaluation pipeline, we constructed large-scale benchmarks of real-world program changes—sourced from 4,084 commits across 20 Java projects—and systematically evaluated two IPA tools for Java. The results demonstrate that, although incremental analysis substantially improves efficiency compared to exhaustive analysis, existing IPA tools exhibit inconsistencies and markedly higher peak memory consumption. Finally, we distill practical insights from our findings to inform future research and development in the field of incremental program analysis.
DOI 原文 ·
@inproceedings{paperbot9248,
title = {Incremental Program Analysis in the Wild: An Empirical Study on Real-World Program Changes},
author = {Xizao Wang and Xiangrong Bin and Lanxin Huang and Shangqing Liu and Jianhua Zhao and Lei Bu},
booktitle = {ASE},
year = {2025},
doi = {10.1109/ase63991.2025.00194}
}