Sociolinguistics Tools in the Analysis of Language Use in Software Development

软件开发中语言使用分析的社会语言学工具

• 批准号: RGPIN-2017-03847
• 负责人: Rice, Jacqueline
• 金额: $ 1.46万
• 依托单位: University of Lethbridge
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=663306
• 关键词: Sociolinguistics; Tools; Analysis; Language; Use

Companies must ensure that software is high quality and understandable, the latter because it is virtually guaranteed that another person will need to read and understand software written by someone else, whether for purposes of upgrading, fixing, or replacing the code. In the field of natural language researchers have used machine learning to determine an author's gender with up to 90% accuracy; this process also allowed the identification of how the two groups used the language differently. This is valuable information for software development teams, whether we look at (for instance) differences between men and women, junior and senior developers, or developers with different native natural languages. While there is a great deal of research on people's use of natural language, both written and spoken, there is very little on how people use artificial languages such as those used for writing software, and none at all on the sociolinguistics of artificial language use. I propose to address this gap with a view to using my discoveries to contribute to improving both software quality and software readability.******One high level motivation of this work lies in the quest for diversity. If different groups use language in different ways, then can tools be tweaked to guide programmers toward more stylistically “neutral” code, thus reducing potential sociolinguistic differences between diverse groups? Or is this defeating the purpose of encouraging a diverse population of contributors and their unique perspectives? When teaching students to code, are there particular styles that might be more “comfortable” for different groups, and should we be encouraging this? We don't know the answers to these questions, but evidence from the natural languages suggests that we should be considering them. ******However as we investigate how a diverse population creates software, the underlying motivation is that of the need for quality code that can be clearly understood. If different groups write code differently, is that contributing to misunderstanding? Are there ways that we can, for instance, “translate” code to make it easier for another reader to understand? If there are significant differences, do these correspond to code quality? Finally, what has larger impact; language use at the micro levels that we are proposing to start with (i.e. line by line), or larger, structural and conceptual differences that form the overall architecture and design of large pieces of software? My research program aims to examine these questions, and I believe that this novel approach will have a significant impact on the field of software engineering.

公司必须确保软件是高质量且易于理解的，后者因为实际上可以保证另一个人需要阅读和理解其他人编写的软件，无论是出于升级、修复还是替换代码的目的。自然语言研究人员使用机器学习来确定作者的性别，准确率高达 90%；这个过程还可以识别两个群体如何以不同的方式使用语言，这对于软件开发团队来说是很有价值的信息，无论我们是否关注。例如）男性和女性、初级和高级之间的差异开发人员或使用不同母语自然语言的开发人员虽然对人们使用书面和口头自然语言进行了大量研究，但关于人们如何使用人工语言（例如用于编写软件的语言）的研究却很少。完全没有涉及人工语言使用的社会语言学，我建议解决这一差距，以期利用我的发现为提高软件质量和软件可读性做出贡献。******这项工作的一个高级动机在于。如果不同的群体以不同的方式使用语言，那么是否可以调整工具来引导程序员编写风格更加“中立”的代码，从而减少不同群体之间潜在的社会语言差异？或者，这是否违背了鼓励多元化贡献者及其独特观点的目的？对于不同群体来说可能更“舒适”的特定风格，我们应该鼓励这一点吗？我们不知道这些问题的答案，但来自自然语言的证据表明我们应该考虑它们***。 ** *然而，当我们调查多元化人口如何创造软件的根本动机是需要可以清楚理解的高质量代码，如果不同的群体编写的代码不同，是否会导致误解？例如，我们是否可以“翻译”代码以使其更容易。让其他读者理解？如果存在显着差异，这些差异是否与我们建议从微观层面（即逐行）或更大的结构层面上的语言使用相对应？和概念上的差异形成了大型的整体架构和设计我的研究计划旨在研究这些问题，我相信这种新颖的方法将对软件工程领域产生重大影响。