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Misery loves company: CrowdStacking traces to aid problem detection

机译:苦难Loves公司:众人追溯到援助问题检测

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During software development, exceptions are by no means exceptional: Programmers repeatedly try and test their code to ensure that it works as expected. While doing so, runtime exceptions are raised, pointing out various issues, such as inappropriate usage of an API, convoluted code, as well as defects. Such failures result in stack traces, lists composed of the sequence of method invocations that led to the interruption of the program. Stack traces are useful to debug source code, and if shared also enhance the quality of bug reports. However, they are handled manually and individually, while we argue that they can be leveraged automatically and collectively to enable what we call crowdstacking, the automated collection of stack traces on the scale of a whole development community. We present our crowdstacking approach, supported by Shore-Line Reporter, a tool which seamlessly collects stack traces during program development and execution and stores them on a central repository. We illustrate how thousands of stack traces stemming from the IDEs of several developers can be leveraged to identify common hot spots in the code that are involved in failures, using this knowledge to retrieve relevant and related bug reports and to provide an effective, instant context of the problem to the developer.
机译:在软件开发期间,例外绝不是尤比:程序员反复尝试并测试其代码以确保它按预期工作。在这样做时,提出了运行时异常,指出各种问题,例如对API的不当使用,复杂的代码以及缺陷。此类失败导致堆栈迹线,列表由导致程序中断的方法调用序列组成。堆栈迹线对调试源代码有用,如果共享也增强了错误报告的质量。但是,它们是手动和单独处理的,同时我们认为它们可以自动杠杆且集体杠杆化,以便在整个开发社区的规模上实现我们所谓的群体。我们介绍了我们的河流报告管理员支持的众群方法,该方法是一种工具,该工具在程序开发和执行期间无缝地收集堆栈迹线,并将它们存储在中央存储库上。我们说明了从若干开发人员的IDE中源的堆栈迹线的迹象是如何利用,以识别涉及故障的代码中的常见热点,使用这些知识来检索相关和相关的错误报告,并提供有效的即时语境开发人员的问题。

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