一句话：综述故障传播失败现象，提出熵损失必要条件的信息论理论，并展望其软件工程应用。
软件执行受到扰动后，下游观察点未必能看到扰动证据；若看不到，则称扰动传播失败。该现象在不同软件工程语境（测试、安全、可靠性、自动代码改进与修复）中以不同名称出现，如偶然正确、正确性吸引、瞬态错误可靠性。论文跨研究孤岛进行综述，并从信息论角度提出一种动态/静态结合的理论：扰动传播失败的必要条件是扰动所在代码区域与后续代码区域组合（或被其接续）后，在全部执行上存在熵损失；熵损失越高，扰动越可能无法传播到下游观察点。作者讨论该理论如何用于指导软件工程实践，并给出研究前景。
If a software execution is disrupted, witnessing the execution at a later point may see evidence of the disruption or not. If not, we say the disruption failed to propagate. One name for this phenomenon is software robustness but it appears in different contexts in software engineering with different names. Contexts include testing, security, reliability, and automated code improvement or repair. Names include coincidental correctness, correctness attraction, transient error reliability. As witnessed, it is a dynamic phenomenon but any explanation with predictive power must necessarily take a static view. As a dynamic/static phenomenon it is convenient to take a statistical view of it which we do by way of information theory. We theorise that for failed disruption propagation to occur, a necessary condition is that the code region where the disruption occurs is composed with or succeeded by a subsequent code region that suffers entropy loss over all executions. The higher is the entropy loss, the higher the likelihood that disruption in the first region fails to propagate to the downstream observation point. We survey different research silos that address this phenomenon and explain how the theory might be exploited in software engineering.
DOI 原文 ·
@inproceedings{paperbot6461,
title = {Software robustness: a survey, a theory, and prospects},
author = {Justyna Petke and David Clark and William B. Langdon},
booktitle = {FSE},
year = {2021},
doi = {10.1145/3468264.3473133}
}