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，后者是因为几乎可以保证另一个人需要阅读和理解其他人所写的软件，无论是出于升级，修复还是替换代码的目的。在自然语言领域，研究人员使用机器学习来确定作者的性别，其精度高达90％。此过程还允许识别两组如何以不同的方式使用该语言。对于软件开发团队来说，这是有价值的信息，无论我们看男女之间的差异，初级和高级开发人员还是具有不同自然语言的开发人员。尽管关于人们使用自然语言的书面和口语有很多研究，但人们如何使用人工语言（例如用于写作软件的人造语言），而对人工语言使用的社会语言学根本没有任何语言。我建议解决这一差距，以期利用我的发现来提高软件质量和软件可读性。******这项工作的一种高级动机在于寻求多样性。如果不同的小组以不同的方式使用语言，那么可以调整工具以指导程序员在风格上更加“中性”代码，从而减少潜在的潜在社会语言差异？还是这是鼓励贡献者及其独特观点的多样性人群的目的？当教学生编码时，是否有特定的样式可能对不同的群体更“舒适”，我们应该鼓励这一点吗？我们不知道这些问题的答案，但是来自自然语言的证据表明我们应该考虑它们。 *****当我们如何研究多样性人群如何创建软件时，基本的动机是需要明确理解的质量代码。如果不同的组以不同的方式编写代码，那会导致误解吗？我们是否可以通过“翻译”代码使其他读者更容易理解？如果存在重大差异，是否对应代码质量？最后，有更大的影响；我们建议从（即按线路）或更大的结构和概念差异开始的微观级别的语言使用，这些差异构成了大型软件的整体体系结构和设计？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。