故障定位精度的人工研究

Zachary P. Fry, Westley Weimer
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引用次数: 50

摘要

定位和修复缺陷是关键的软件工程活动。然而,并不是所有的程序和错误都同样容易调试。我们提出了正式的模型,由一项涉及65名参与者(来自学术界和工业界)的人类研究和1830个总判断支持,将各种软件和缺陷相关的特征与人类定位错误的准确性联系起来。我们的研究涉及Java教科书中的示例代码,帮助我们控制可读性和复杂性。我们发现某些类型的缺陷对人类来说很难准确定位。例如,根据实验观察,人类定位“额外语句”的准确率是“缺失语句”的五倍以上。我们还发现,与所涉及的缺陷类型无关,某些代码上下文比其他代码上下文更难调试。例如,在提供数组抽象的代码中,人类发现缺陷的准确度是提供树抽象的代码的三倍以上。我们识别和分析了预测人类故障定位准确性的代码特征。最后,我们提出了一个正式的调试精度模型,该模型基于那些与人类性能具有统计上显著相关性的源代码特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A human study of fault localization accuracy
Localizing and repairing defects are critical software engineering activities. Not all programs and not all bugs are equally easy to debug, however. We present formal models, backed by a human study involving 65 participants (from both academia and industry) and 1830 total judgments, relating various software- and defect-related features to human accuracy at locating errors. Our study involves example code from Java textbooks, helping us to control for both readability and complexity. We find that certain types of defects are much harder for humans to locate accurately. For example, humans are over five times more accurate at locating “extra statements” than “missing statements” based on experimental observation. We also find that, independent of the type of defect involved, certain code contexts are harder to debug than others. For example, humans are over three times more accurate at finding defects in code that provides an array abstraction than in code that provides a tree abstraction. We identify and analyze code features that are predictive of human fault localization accuracy. Finally, we present a formal model of debugging accuracy based on those source code features that have a statistically significant correlation with human performance.
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