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，后者是因为它实际上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、修复还是替换代码。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，准确率高达90%；这一过程也有助于识别两组人使用语言的不同之处。这对于软件开发团队来说是有价值的信息，无论我们观察（例如）男性和女性、初级和高级开发人员之间的差异，还是使用不同自然语言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，包括书面语言和口头语言，但很少有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，而且根本没有关于人工语言使用的社会语言学研究。我建议用我的发现来解决这个差距，以提高软件质量和软件可读性。

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.