学习易处理的故障定位概率模型
Learning Tractable Probabilistic Models for Fault Localization
摘要：
近年来，一些概率技术已被应用于各种调试问题。然而，大多数现有的概率调试系统使用相对简单的统计模型，并未能推广到多个程序。在这项工作中，我们提出了可处理的故障定位模型（TFLMs），可以从数据中学习，并概率地推断错误的位置。虽然大多数以前的统计调试方法都是在单个程序的多次执行上进行推广，但TFLM是在以前看到的错误程序的语料库上进行训练的，并学会识别错误的重复模式。广泛使用的故障定位技术，如TARANTALY评估孤立的每条线路的可疑性;相比之下，TFLM定义了每条线路的故障指标变量的联合概率分布。具有丰富依赖结构的联合分布通常在计算上是难以处理的; TFLM通过利用易处理的概率模型（特别是关系SPN）的最新发展来避免这一点。此外，TFLM还可以包含其他信息源，包括基于覆盖范围的功能，如TARANTTM。我们评估故障定位性能的TFLM，包括TARANTHALDS分数的概率模型中的功能。我们的研究表明，学习TFLM隔离错误更有效地比以前的统计方法或直接使用TARANTFLOW。
英文摘要：
In recent years, several probabilistic techniques have been applied to various debugging problems. However, most existing probabilistic debugging systems use relatively simple statistical models, and fail to generalize across multiple programs. In this work, we propose Tractable Fault Localization Models (TFLMs) that can be learned from data, and probabilistically infer the location of the bug. While most previous statistical debugging methods generalize over many executions of a single program, TFLMs are trained on a corpus of previously seen buggy programs, and learn to identify recurring patterns of bugs. Widely-used fault localization techniques such as TARANTULA evaluate the suspiciousness of each line in isolation; in contrast, a TFLM defines a joint probability distribution over buggy indicator variables for each line. Joint distributions with rich dependency structure are often computationally intractable; TFLMs avoid this by exploiting recent developments in tractable probabilistic models (specifically, Relational SPNs). Further, TFLMs can incorporate additional sources of information, including coverage-based features such as TARANTULA. We evaluate the fault localization performance of TFLMs that include TARANTULA scores as features in the probabilistic model. Our study shows that the learned TFLMs isolate bugs more effectively than previous statistical methods or using TARANTULA directly.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
