概率编程系统中的Bug模式

2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C) Pub Date : 2022-12-01 DOI:10.1109/QRS-C57518.2022.00063

Shoma Hamada, Haibo Yu, Vo Dai Trinh, Yuri Nishimura, Jianjun Zhao

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引用次数: 0

摘要

概率编程系统允许开发人员对随机现象进行建模，并有效地对模型进行推理。随着概率规划系统数量的显著增长和应用的日益广泛，这种系统的可靠性变得非常重要。为了开发高效的概率编程系统bug检测工具，分析现有类似系统的真实bug至关重要。本文对一个真实的概率编程系统PyMC3的bug及其特征进行了实证研究。在271个已关闭的错误中，我们确定了20个概率编程语言特有的错误，并从这些错误中提取了8个错误模式。结果表明，许多错误是由类型引起的。我们还提出了一些自动检测这些错误模式的可能方法。预计这将有助于通过捕获实际概率程序中的错误特征来开发错误检测工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Bug Patterns in Probabilistic Programming Systems

Probabilistic programming systems allow developers to model random phenomena and perform reasoning about the model efficiently. As the number of probabilistic programming systems is growing significantly and are used more and more widely, the reliability of such systems is becoming very important. It is crucial to analyze real bugs of existing similar systems in order to develop efficient bug detection tools for probabilistic programming systems. This paper conducts an empirical study investigating bugs and their features on PyMC3, a real probabilistic programming system. Among 271 closed bugs, we identified 20 bugs that are unique to probabilistic programming languages and extracted eight bug patterns from these bugs. The result showed that many of the bugs were caused by types. We also propose some possible methods for automatically detecting these bug patterns. It is expected that this will contribute to the development of bug detection tools by capturing the characteristics of bugs in actual probabilistic programs in the future.

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2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)

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