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An Empirical Study on Practicality of Specification Mining Algorithms on a Real-World Application

机译:实际应用规范挖掘算法实用性的实证研究

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Dynamic model inference techniques have been the center of many research projects recently. There are now multiple open source implementations of state-of-the-art algorithms, which provide basic abstraction and merging capabilities. Most of these tools and algorithms have been developed with one particular application in mind, which is program comprehension. The output models can abstract away the details of the program and represent the software behaviour in a concise and easy to understand form. However, one application context that is less studied is using such inferred models for debugging, where the behaviour to abstract is a faulty behaviour (e.g., a set of execution traces including a failed test case). We tried to apply some of the existing model inference techniques in a real-world industrial context to support program comprehension for debugging. Our initial experiments have shown many limitations both in terms of implementation as well as the algorithms. The paper will discuss the root cause of the failures and proposes ideas for future improvement.
机译:动态模型推理技术最近是许多研究项目的中心。现在有多种开源实现的最先进的算法,它提供基本的抽象和合并功能。大多数这些工具和算法都是通过考虑的一个特定应用而开发的,这是程序理解。输出模型可以摘要程序的细节,并以简洁且易于理解的形式代表软件行为。然而,研究的一个应用程序上下文是使用这种推断模型来调试,其中抽象的行为是错误的行为(例如,一组执行迹线,包括失败的测试用例)。我们试图在真实的工业背景中应用一些现有的模型推理技术,以支持调试程序的程序理解。我们的初步实验在实现和算法方面都显示了许多限制。本文将讨论失败的根本原因,并提出了未来改进的想法。

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