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Understanding the complexity embedded in large routine call traces with a focus on program comprehension tasks

机译：了解大型例程调用跟踪中嵌入的复杂性，并着重于程序理解任务

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The analysis of execution traces has been shown to be useful in many software maintenance activities that require a certain understanding of the systems¿ behaviour. Traces, however, are extremely large, hence are difficult for humans to analyse without effective tools. These tools usually support some sort of trace abstraction techniques that can help users understand the essence of a trace despite the trace being massive. Designing such tools requires a good understanding of the amount of complexity embedded in traces. Trace complexity has traditionally been measured using the file size or the number of lines in the trace. In this study, the authors argue that such metrics provide limited indication of the complexity of a trace. The authors address this issue by presenting a catalogue of metrics for assessing the various facets of traces of routine calls, with the ultimate objective being to facilitate the development of tools for the exploration of lengthy traces. The authors show the effectiveness of our metrics by applying them to 35 traces generated from four software systems.
机译：执行痕迹的分析已被证明在许多软件维护活动中很有用，这些活动需要对系统的行为有一定的了解。但是，痕迹非常大，因此如果没有有效的工具，人类将很难进行分析。这些工具通常支持某种形式的跟踪抽象技术，即使跟踪量很大，也可以帮助用户理解跟踪的本质。设计此类工具需要充分了解跟踪中嵌入的复杂性数量。传统上，跟踪复杂度是使用文件大小或跟踪中的行数来衡量的。在这项研究中，作者认为，此类度量标准只能提供痕量复杂性的有限指示。为了解决这个问题，作者提出了一系列用于评估例行调用痕迹各个方面的度量标准，其最终目的是促进开发用于探索冗长痕迹的工具。作者通过将它们应用到从四个软件系统生成的35条迹线中，展示了我们的度量标准的有效性。

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