Bug签名最小化和融合

D. Lo, Hong Cheng, Xiaoyin Wang
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引用次数: 6

摘要

调试是一项耗时的活动。为了帮助调试,已经提出了许多方法来精确定位给定标记失败的错误位置并纠正执行。虽然这些方法已被证明是准确的,但有时仅靠位置不足以帮助程序员理解错误发生的原因以及如何修复它。此外,单个位置可能不足以区分失败和正确执行。为了解决上述挑战,最近有一些关于提取bug签名的研究,这些签名由多个位置以特定顺序出现在一起表示bug的发生。Cheng等人对bug签名的最新研究将程序执行建模为图形。然后对比对应于失败和正确执行的两组图,以提取最具判别性的连接子图,作为错误签名。然而,有两个限制:(1)返回的签名可能不是最小的;(2)它们只能捕获本地化的错误上下文。在这项工作中,我们开发了一种签名最小化技术来捕获最小的判别签名。此外,我们还提出了一种签名融合技术来融合断开的子图,以便我们的方法可以捕获跨越多个位置的错误上下文。在Siemens和Space数据集上的实验研究表明了所提出的缺陷特征最小化和融合技术的有效性。与最先进的漏洞签名挖掘技术相比,我们减少了高达57.7%的漏洞数量,减少了高达85.6%的平均遍历节点数量。
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Bug Signature Minimization and Fusion
Debugging is a time-consuming activity. To help in debugging, many approaches have been proposed to pinpoint the location of errors given labeled failures and correct executions. While such approaches have been shown to be accurate, at times the location alone is not sufficient in helping programmers understand why the bug happens and how to fix it. Furthermore, a single location might not be powerful enough to discriminate failures from correct executions. To address the above challenges, there have been recent studies on extracting bug signatures which are composed of multiple locations appearing together in a particular order signifying an occurrence of a bug. The latest study on bug signatures by Cheng et al. models program executions as graphs. Two sets of graphs corresponding to failures and correct executions are then contrasted to extract the most discriminative connected sub graphs serving as bug signatures. However, there are two limitations: (1) returned signatures might not be minimal and (2) they can only capture localized bug context. In this work, we develop a signature minimization technique to capture minimal discriminative signatures. Also, we propose a technique of signature fusion to fuse disconnected sub graphs so that our method can capture bug contexts spanning multiple locations. Experimental study on Siemens and Space dataset shows the effectiveness of the proposed bug signature minimization and fusion techniques. Comparing with the state-of-the-art bug signature mining technique, we reduce the number of bugs missed by up to 57.7%, and reduce the average number of nodes traversed by up to 85.6%.
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