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一种利用潜在分歧软件故障定位的框架

A Novel Framework for Locating Software Faults Using Latent Divergences
课程网址: http://videolectures.net/ecmlpkdd2011_khurshid_divergences/  
主讲教师: Sarfraz Khurshid
开课单位: 德克萨斯大学
开课时间: 2011-11-30
课程语种: 英语
中文简介:
故障定位,即在错误的程序中识别错误的代码行,是一个冗长的过程,这通常需要大量的手工工作,而且成本高昂。近年来,在自动故障定位技术方面取得了很大进展,特别是使用程序谱——执行失败和通过的测试运行为隔离故障提供了基础。尽管取得了进展,但是大型程序中的故障定位仍然是一个具有挑战性的问题,因为即使在大型问题中检查代码行的一小部分,也需要大量的手工工作。本文提出了一种新的基于潜在发散的故障定位框架——一种有效的机器学习特征选择方法。我们的观点是,故障定位问题可以归结为特征选择问题,即代码行与特征对应。我们还介绍了使用西门子主题程序套件对我们的框架进行的实验评估,这是研究软件工程中故障定位技术的标准基准。结果表明,我们的框架比现有的技术能够实现更精确的故障定位。
课程简介: Fault localization, i.e., identifying erroneous lines of code in a buggy program, is a tedious process, which often requires considerable manual effort and is costly. Recent years have seen much progress in techniques for automated fault localization, specifically using program spectra - executions of failed and passed test runs provide a basis for isolating the faults. Despite the progress, fault localization in large programs remains a challenging problem, because even inspecting a small fraction of the lines of code in a large problem can require substantial manual effort. This paper presents a novel framework for fault localization based on latent divergences - an effective method for feature selection in machine learning. Our insight is that the problem of fault localization can be reduced to the problem of feature selection, where lines of code correspond to features. We also present an experimental evaluation of our framework using the Siemens suite of subject programs, which are a standard benchmark for studying fault localization techniques in software engineering. The results show that our framework enables more accurate fault localization than existing techniques.
关 键 词: 计算机科学; 机器学习; 模式识别
课程来源: 视频讲座网
最后编审: 2020-06-08:yumf
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