了解在Linux内核邮件列表中对侵略性的低级别内部协议

IF 3.7 2区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Thomas Bock , Niklas Schneider , Angelika Schmid , Sven Apel , Janet Siegmund
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引用次数: 0

摘要

在开源软件(OSS)项目中,软件开发人员之间的通信起着至关重要的作用。不出所料,先前的研究已经表明,谈话的语气,特别是侵略性会影响开发人员在OSS项目中的参与。因此,我们的目标是研究Linux内核邮件列表(LKML)上的攻击性通信行为,该列表以其一些贡献者的攻击性电子邮件而闻名。为此,我们试图通过涉及多个注释者的人类注释研究来评估来自LKML的720封电子邮件的攻击性程度,以选择合适的情感分析工具。我们的注释研究结果表明,即使在人类中,也存在着实质性的分歧,这揭示了在软件工程领域研究攻击性的更深层次的方法论挑战。调整了我们的关注点,我们进行了更深入的研究,并基于对评级模糊的电子邮件的人工调查,调查了为什么人类之间的一致性普遍较低。我们的研究结果表明,人类的感知是个体的和环境相关的,特别是当涉及到技术内容时。因此,在识别软件工程文本中的攻击性时,依赖于人工注释的聚合度量是不够的。因此,专门训练人类注释数据的情感分析工具不一定与人类对攻击性的感知相匹配,相应的结果需要有所保留。通过报告我们的结果和经验,我们的目标是在研究软件工程领域的侵略性(和情感,一般来说)时,确认并提高对这种方法挑战的额外认识。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Understanding the low inter-rater agreement on aggressiveness on the Linux Kernel Mailing List
Communication among software developers plays an essential role in open-source software (OSS) projects. Not unexpectedly, previous studies have shown that the conversational tone and, in particular, aggressiveness influence the participation of developers in OSS projects. Therefore, we aimed at studying aggressive communication behavior on the Linux Kernel Mailing List (LKML), which is known for aggressive e-mails of some of its contributors. To that aim, we attempted to assess the extent of aggressiveness of 720 e-mails from the LKML with a human annotation study, involving multiple annotators, to select a suitable sentiment analysis tool.
The results of our annotation study revealed that there is substantial disagreement, even among humans, which uncovers a deeper methodological challenge of studying aggressiveness in the software-engineering domain. Adjusting our focus, we dug deeper and investigated why the agreement among humans is generally low, based on manual investigations of ambiguously rated e-mails. Our results illustrate that human perception is individual and context dependent, especially when it comes to technical content. Thus, when identifying aggressiveness in software-engineering texts, it is not sufficient to rely on aggregated measures of human annotations. Hence, sentiment analysis tools specifically trained on human-annotated data do not necessarily match human perception of aggressiveness, and corresponding results need to be taken with a grain of salt. By reporting our results and experience, we aim at confirming and raising additional awareness of this methodological challenge when studying aggressiveness (and sentiment, in general) in the software-engineering domain.
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来源期刊
Journal of Systems and Software
Journal of Systems and Software 工程技术-计算机:理论方法
CiteScore
8.60
自引率
5.70%
发文量
193
审稿时长
16 weeks
期刊介绍: The Journal of Systems and Software publishes papers covering all aspects of software engineering and related hardware-software-systems issues. All articles should include a validation of the idea presented, e.g. through case studies, experiments, or systematic comparisons with other approaches already in practice. Topics of interest include, but are not limited to: •Methods and tools for, and empirical studies on, software requirements, design, architecture, verification and validation, maintenance and evolution •Agile, model-driven, service-oriented, open source and global software development •Approaches for mobile, multiprocessing, real-time, distributed, cloud-based, dependable and virtualized systems •Human factors and management concerns of software development •Data management and big data issues of software systems •Metrics and evaluation, data mining of software development resources •Business and economic aspects of software development processes The journal welcomes state-of-the-art surveys and reports of practical experience for all of these topics.
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