Sociolinguistics Tools in the Analysis of Language Use in Software Development

公司必须确保软件是高质量的和可理解的，后者是因为它几乎保证了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，修复或替换代码。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%;这个过程也允许识别两个群体如何不同地使用语言。这对软件开发团队来说是有价值的信息，无论我们是在看（例如）男性和女性之间的差异，初级和高级开发人员之间的差异，还是使用不同原生自然语言的开发人员之间的差异。虽然有很多关于人们使用自然语言的研究，包括书面和口语，但很少有人研究人们如何使用人工语言，例如用于编写软件的人工语言，而且根本没有关于人工语言使用的社会语言学。我建议解决这个差距，以期利用我的发现来提高软件质量和软件可读性。这项工作的一个高层次的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风格更“中性”的代码，从而减少不同群体之间潜在的社会语言学差异？或者，这是否违背了鼓励多样化的贡献者及其独特观点的目的？在教学生编码时，是否有特定的风格可能对不同的群体更“舒适”，我们是否应该鼓励这种风格？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，当我们调查不同的人群如何创建软件时，潜在的动机是需要可以清楚理解的高质量代码。如果不同的团队编写不同的代码，这会导致误解吗？例如，我们有没有办法“翻译”代码，让其他读者更容易理解？如果存在显著差异，这些差异是否与代码质量相对应？最后，什么有更大的影响;我们建议从微观层面（即逐行）开始的语言使用，还是更大的，形成大型软件的整体架构和设计的结构和概念差异？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.