Understanding the use of spectrum-based fault localization

IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Journal of Software-Evolution and Process Pub Date : 2023-10-11 DOI:10.1002/smr.2622
Higor Amario de Souza, Marcelo de Souza Lauretto, Fabio Kon, Marcos Lordello Chaim
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Abstract

Developers spend significant time locating and fixing bugs, which is often performed manually. Although spectrum-based fault localization (SFL) techniques aim at helping developers to locate faults, they are not yet used in practice. Recent studies have investigated how developers use SFL, presenting different conclusions about their effectiveness and usefulness. We carried out a user study to further enhance the understanding of SFL. We assessed whether SFL improves the developers' performance and to what extent SFL leads developers to inspect faulty code excerpts. We also investigated the intention of the developers to use SFL and how they interact with SFL. Twenty-six participants performed debugging tasks using real programs, with and without using the Jaguar SFL tool. Using SFL, more developers located and fixed the bugs. SFL also led more developers to inspect the faulty code and locate the faulty method. However, they did not spend less time locating the faults. SFL was well-accepted by the participants, who showed intention to use it in their daily activities. Our results indicate that SFL is useful even when the fault is not ranked among the first positions, leading developers to reach faulty code regions and find the bugs.

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了解基于频谱的故障定位的使用情况
开发人员需要花费大量时间来定位和修复故障,而这通常需要手动完成。尽管基于频谱的故障定位(SFL)技术旨在帮助开发人员定位故障,但它们尚未被实际使用。最近的研究调查了开发人员如何使用 SFL,对其有效性和实用性提出了不同的结论。我们开展了一项用户研究,以进一步加深对 SFL 的理解。我们评估了 SFL 是否提高了开发人员的性能,以及 SFL 在多大程度上引导开发人员检查有问题的代码摘录。我们还调查了开发人员使用 SFL 的意向以及他们如何与 SFL 互动。26 名参与者在使用或不使用 Jaguar SFL 工具的情况下,使用真实程序执行了调试任务。使用 SFL,更多的开发人员找到并修复了错误。SFL 还让更多的开发人员检查了有问题的代码并找到了有问题的方法。但是,他们并没有减少查找错误的时间。SFL 得到了参与者的广泛认可,他们表示有意在日常活动中使用 SFL。我们的结果表明,即使故障没有排在第一位,SFL 也是有用的,它能引导开发人员到达故障代码区域并找到错误。
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来源期刊
Journal of Software-Evolution and Process
Journal of Software-Evolution and Process COMPUTER SCIENCE, SOFTWARE ENGINEERING-
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10.00%
发文量
109
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Issue Information Issue Information A hybrid‐ensemble model for software defect prediction for balanced and imbalanced datasets using AI‐based techniques with feature preservation: SMERKP‐XGB Issue Information LLMs for science: Usage for code generation and data analysis
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