With the development of information technology such as cloud computing, IoT, etc, software becomes the infrastructure. On the one hand, it is critical to ensure the reliability of software, on the other, sample code can be mined from open source software to provide reference for automatic debugging. Most of existing automated debugging researches are based on the assumption that defect programs are nearly correct, therefore they can successfully pass some test cases and fail to execute others. However, this assumption sometimes does not hold. In view of the fact that a programs may fail for all the given test cases in the test suite, but there are a large number of examples available for reference, a sample based fault localization method is studied. A fault localization method by analyzing the context of failure propagation is proposed, which locates suspicious statements by identifying the execution status and structural semantics differences between the defective program and sample program. Through the interactive analysis of value sequences and structure semantics, the influence of code variations and failure propagation is reduced. The experimental results have shown that the method can effectively locate suspicious statements and provide assistance for defect repair when there are enough sample programs.

中文翻译：

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通过在云计算环境中使用样本分析故障传播来进行故障定位

随着诸如云计算，物联网等信息技术的发展，软件成为基础架构。一方面，确保软件的可靠性至关重要，另一方面，可以从开源软件中提取示例代码，以为自动调试提供参考。现有的大多数自动化调试研究都基于缺陷程序几乎正确的假设，因此它们可以成功通过某些测试用例而无法执行其他测试用例。但是，这种假设有时不成立。鉴于程序可能会针对测试套件中的所有给定测试用例失败，但有大量示例可供参考，因此研究了一种基于样本的故障定位方法。通过分析故障传播的背景，提出了一种故障定位方法，它通过识别缺陷程序和示例程序之间的执行状态和结构语义差异来查找可疑语句。通过对值序列和结构语义进行交互分析，可以减少代码变化和故障传播的影响。实验结果表明，该方法可以有效地定位可疑语句，并在有足够的示例程序的情况下为缺陷修复提供帮助。