持续软件开发中的文档方法

Theo Theunissen, S. Hoppenbrouwers, S. Overbeek
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摘要

对于行业从业者以及ICT/CS学生来说,将关于软件产品的写作和阅读保持在最低限度是一种常见的做法。然而,不编写文档可能会导致与设计、构建和维护阶段所做决策相关的知识蒸发相关的严重问题。在本文中,我们区分了启动项目或迭代所需的前期知识,完成项目或迭代所需的知识,以及操作和维护软件产品所需的知识。“知识”指的是可操作的信息。我们提出了三种方法来跟上现代开发方法,以防止软件项目中知识蒸发的风险。这些方法是“前期足够”文档、“可执行知识”和“自动文本分析”,以帮助记录、证实、管理和检索上述阶段的设计决策。“前期足够”文档的主要特征是,前期所需的知识包括形成思想/想法、(子)系统之间的编码接口描述和计划。为了构建软件并最大限度地利用渐进式洞察力,更新规范就足够了。他人使用、操作和维护产品所需的知识包括详细的设计和结果的责任。“可执行知识”指的是除源代码以外的任何可执行工件。主要工件包括测试驱动开发方法和基础设施即代码,包括持续集成脚本。第三种方法涉及“自动文本分析”,使用文本挖掘和深度学习来检索设计决策。
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Approaches for Documentation in Continuous Software Development
It is common practice for practitioners in industry as well as for ICT/CS students to keep writing – and reading ¬– about software products to a bare minimum. However, refraining from documentation may result in severe issues concerning the vaporization of knowledge regarding decisions made during the phases of design, build, and maintenance. In this article, we distinguish between knowledge required upfront to start a project or iteration, knowledge required to complete a project or iteration, and knowledge required to operate and maintain software products. With `knowledge', we refer to actionable information. We propose three approaches to keep up with modern development methods to prevent the risk of knowledge vaporization in software projects. These approaches are `Just Enough Upfront' documentation, `Executable Knowledge', and `Automated Text Analytics' to help record, substantiate, manage and retrieve design decisions in the aforementioned phases. The main characteristic of `Just Enough Upfront' documentation is that knowledge required upfront includes shaping thoughts/ideas, a codified interface description between (sub)systems, and a plan. For building the software and making maximum use of progressive insights, updating the specifications is sufficient. Knowledge required by others to use, operate and maintain the product includes a detailed design and accountability of results. `Executable Knowledge' refers to any executable artifact except the source code. Primary artifacts include Test Driven Development methods and infrastructure-as-code, including continuous integration scripts. A third approach concerns `Automated Text Analysis' using Text Mining and Deep Learning to retrieve design decisions.
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