Sociolinguistics Tools in the Analysis of Language Use in Software Development

公司必须确保软件是高质量和可理解的，后者是因为几乎可以保证另一个人将需要阅读和理解其他人编写的软件，无论是为了升级、修复还是替换代码。在自然语言领域，研究人员已经使用机器学习来确定作者的性别，准确率高达90%；这一过程还允许识别两组人如何使用不同的语言。这对于软件开发团队来说是很有价值的信息，无论我们看(例如)男性和女性、初级开发人员和高级开发人员之间的差异，还是使用不同母语的开发人员之间的差异。虽然有大量关于人们使用自然语言的研究，无论是书面语言还是口头语言，但关于人们如何使用人工语言(如用于编写软件的语言)的研究很少，更没有关于人工语言使用的社会语言学的研究。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。*这项工作的一个高层次动机在于寻求多样性。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更具风格的“中立”代码，从而减少不同群体之间潜在的社会语言差异？或者，这是否违背了鼓励不同的贡献者及其独特观点的目的？在教学生编程时，有没有特定的风格可能更适合不同的群体，我们应该鼓励这样做吗？我们不知道这些问题的答案，但来自自然语言的证据表明，我们应该考虑这些问题。*然而，当我们调查不同群体如何创造软件时，潜在的动机是对能够清楚理解的高质量代码的需求。如果不同的组编写不同的代码，这是否会导致误解？例如，我们有没有办法可以“翻译”代码，让其他读者更容易理解？如果存在显著差异，这些差异是否对应于代码质量？最后，什么影响更大；是我们建议开始的微观层面的语言使用(即逐行)，还是形成大型软件整体架构和设计的更大的结构和概念差异？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。

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.