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Behavior-Informed Algorithms for Automatic Documentation Generation

机译:自动文档生成的行为知识算法

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Programmers rely on source code documentation to quickly understand what the source code does and how they would use it. Unfortunately, many programmers do not have the time to write and maintain source code documentation. A solution to this problem is to document and summarize source code automatically. Unfortunately, research efforts to automatically generate documentation have stalled recently because the research community does not know exactly what a summary of source code should include. To solve this problem, my overall strategy is to study programmer behavior in order to write algorithms that mimic that behavior. I have four key areas of work in which I execute that strategy: First, I determine what areas of code programmers read when they create documentation. Second, I find patterns in programmers' eye movements when they reading code. Third, I use recordings of developer-client meetings to extract user story information. Finally, I propose to conduct a grounded theory study at a medium sized software company to determine whether factors outside the code influence source code summarization. This paper discusses the foundation for my career in the software engineering community, and I seek the community's advice.
机译:程序员依靠源代码文档来快速理解源代码的表现以及它们如何使用它。不幸的是,许多程序员没有时间编写和维护源代码文档。解决此问题的解决方案是自动记录和汇总源代码。遗憾的是,最近,自动生成文档的研究工作已经停止了,因为研究社区不知道源代码摘要应该包括什么。为了解决这个问题,我的整体策略是研究程序员行为,以便编写模仿该行为的算法。我有四个关键的工作领域,我执行该策略:首先,我确定在创建文档时读取的代码程序员的哪些方面。其次,当他们阅读代码时,我发现程序员眼球运动中的模式。第三,我使用开发人员会议的录音来提取用户故事信息。最后,我建议在中型软件公司进行接地的理论研究,以确定代码外的因素是否会影响源代码摘要。本文讨论了我在软件工程界的职业生涯的基础,我寻求社区的建议。

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