一句话：系统介绍动态数据竞争检测与预测分析的基础、理论及实践，强调可靠报告未观测执行中的竞争。
数据竞争是最常见的并发缺陷，已有大量工作致力于保障无数据竞争程序。主流方法依赖动态分析，通过分析程序执行可靠地报告 DRF 违规。近期研究转向预测分析：在未观测到的程序执行中预测 DRF 违规，同时保证分析可靠性（不产生误报）。本教程围绕这一方向，梳理动态数据竞争预测的基础、理论与工程实践。教程旨在帮助研究者与开发者理解现有动态检测技术的可靠性保证、预测能力及其适用场景，并概览该领域从基础概念到理论模型再到实际工具的关键进展。
Data races are the most common concurrency bugs and considerable efforts are put in ensuring data-race-free (DRF) programs. The most popular approach is via dynamic analyses, which soundly report DRF violations by analyzing program executions. Recently, there has been a prevalent shift to predictive analysis techniques. Such techniques attempt to predict DRF violations even in unobserved program executions, while making sure that the analysis is sound (does not raise false positives).
DOI 原文 ·
@inproceedings{paperbot7063,
title = {Dynamic data race prediction: fundamentals, theory, and practice (tutorial)},
author = {Umang Mathur and Andreas Pavlogiannis},
booktitle = {FSE},
year = {2022},
doi = {10.1145/3540250.3569450}
}