一句话：提出 Storm，通过程序归约帮助定位概率程序在概率编程系统中引发失败的代码片段。
概率编程语言将复杂概率模型表达为直观的概率程序，而概率编程系统（PP systems）对开发者隐藏推断算法细节。然而，当系统运行过程中出现失败时，开发者往往缺乏支持来找出概率程序中触发该失败的具体部分。标题与摘要表明，论文提出 Storm，一种面向概率编程系统测试与调试的程序归约技术，旨在把导致失败的概率程序缩小为更小且仍能复现该失败的程序，从而帮助定位故障来源。摘要未给出方法的具体实现细节、评估指标或实验结果，因此无法从现有信息判断其归约效果与适用范围。
Probabilistic programming languages offer an intuitive way to model uncertainty by representing complex probability models as simple probabilistic programs. Probabilistic programming systems (PP systems) hide the complexity of inference algorithms away from the program developer. Unfortunately, if a failure occurs during the run of a PP system, a developer typically has very little support in finding the part of the probabilistic program that causes the failure in the system.
@inproceedings{paperbot5179,
title = {Storm: program reduction for testing and debugging probabilistic programming systems},
author = {Saikat Dutta and Wenxian Zhang and Zixin Huang and Sasa Misailovic},
booktitle = {FSE},
year = {2019},
doi = {10.1145/3338906.3338972}
}