基于代币的微提交实证研究

IF 3.5 2区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING Empirical Software Engineering Pub Date : 2024-09-04 DOI:10.1007/s10664-024-10527-8
Masanari Kondo, Daniel M. German, Yasutaka Kamei, Naoyasu Ubayashi, Osamu Mizuno
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引用次数: 0

摘要

在软件开发过程中,开发人员经常会对源代码进行维护,通过一次提交改变几行代码。充分了解此类小改动的特点可以为质量保证方法(如自动程序修复)提供支持,因为小改动很可能是为了解决其他改动中的缺陷;因此,了解产生小改动的原因有助于了解引入错误的类型。最终,这些原因和错误类型可用于加强质量保证方法,以提高代码质量。虽然之前的研究使用代码搅动来描述和研究小改动,但这样的定义有很大的局限性。具体来说,它丢失了一行中已更改标记的信息。例如,该定义无法区分以下两种单行更改:(1) 更改字符串字面以修复显示的信息;(2) 更改函数调用并添加新参数。这些无疑都是维护活动,但我们推断,研究人员和从业人员更感兴趣的是支持后一种变更。为了解决这一局限性,我们在本文中定义了微提交(micro commits),一种基于变更标记的小变更类型。我们的目标是使用已更改标记来量化小变更。已更改标记让我们能更精确地识别小变更。事实上,这种标记级定义可以区分上述例子。我们对四个开放源码软件项目中定义的微提交进行了调查,并了解了它们的特点,这是首次对基于令牌的微提交进行实证研究。我们发现,微提交主要替换单个名称或字面标记,而且微提交更有可能用于修复错误。此外,我们还建议使用基于标记的信息来支持软件工程方法,在这些方法中,非常小的改动会显著影响其有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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An empirical study of token-based micro commits

In software development, developers frequently apply maintenance activities to the source code that change a few lines by a single commit. A good understanding of the characteristics of such small changes can support quality assurance approaches (e.g., automated program repair), as it is likely that small changes are addressing deficiencies in other changes; thus, understanding the reasons for creating small changes can help understand the types of errors introduced. Eventually, these reasons and the types of errors can be used to enhance quality assurance approaches for improving code quality. While prior studies used code churns to characterize and investigate the small changes, such a definition has a critical limitation. Specifically, it loses the information of changed tokens in a line. For example, this definition fails to distinguish the following two one-line changes: (1) changing a string literal to fix a displayed message and (2) changing a function call and adding a new parameter. These are definitely maintenance activities, but we deduce that researchers and practitioners are interested in supporting the latter change. To address this limitation, in this paper, we define micro commits, a type of small change based on changed tokens. Our goal is to quantify small changes using changed tokens. Changed tokens allow us to identify small changes more precisely. In fact, this token-level definition can distinguish the above example. We investigate defined micro commits in four OSS projects and understand their characteristics as the first empirical study on token-based micro commits. We find that micro commits mainly replace a single name or literal token, and micro commits are more likely used to fix bugs. Additionally, we propose the use of token-based information to support software engineering approaches in which very small changes significantly affect their effectiveness.

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来源期刊
Empirical Software Engineering
Empirical Software Engineering 工程技术-计算机:软件工程
CiteScore
8.50
自引率
12.20%
发文量
169
审稿时长
>12 weeks
期刊介绍: Empirical Software Engineering provides a forum for applied software engineering research with a strong empirical component, and a venue for publishing empirical results relevant to both researchers and practitioners. Empirical studies presented here usually involve the collection and analysis of data and experience that can be used to characterize, evaluate and reveal relationships between software development deliverables, practices, and technologies. Over time, it is expected that such empirical results will form a body of knowledge leading to widely accepted and well-formed theories. The journal also offers industrial experience reports detailing the application of software technologies - processes, methods, or tools - and their effectiveness in industrial settings. Empirical Software Engineering promotes the publication of industry-relevant research, to address the significant gap between research and practice.
期刊最新文献
The effect of data complexity on classifier performance. Reinforcement learning for online testing of autonomous driving systems: a replication and extension study. An empirical study on developers’ shared conversations with ChatGPT in GitHub pull requests and issues Quality issues in machine learning software systems An empirical study of token-based micro commits
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