Software Development (SD) is remarkably dynamic and is critically dependent on the knowledge acquired by the project's software developers as the project progresses. Software developers need to understand large amounts of information related to the tasks at hand. This information (context) is often not explicit, as it can be lost in large documentation repositories, a team member's brain, or beyond their cognitive memory capacity. These contexts include tool features, integration strategies, data structures, code syntax, approaches to tasks, project definitions, and even implicit or tacit contexts, which add significant complexity to the SD process. Current software development practices still lack sufficient techniques using the existing SD execution information and context to provide developers with relevant process guidance, augmenting their capacity to do their job using available applicable information. This paper presents ongoing and future research on an approach to support conversational agent-based knowledge-augmented software development. Developers benefit by receiving recommendations about task-related information and workflows they need to execute. This work advances human-computer interaction patterns in workflow engines, from graphical user interfaces to conversational patterns in software engineering.

翻译：软件开发(SD)具有显著的动态性，且高度依赖于项目进展过程中开发者所获取的知识。开发者需要理解大量与当前任务相关的信息。这些信息（即上下文）往往并不明确，可能散落在庞大的文档库中、团队成员的大脑中，或超出其认知记忆的承载能力。这些上下文包括工具特性、集成策略、数据结构、代码语法、任务处理方法、项目定义，甚至隐含或隐性知识，显著增加了软件开发过程的复杂性。当前软件开发实践仍缺乏利用现有SD执行信息与上下文为开发者提供相关过程指导的充分技术，以增强其利用可用信息完成工作的能力。本文提出一项进行中及未来研究，旨在探索一种支持基于对话式智能体的知识增强型软件开发方法。开发者通过接收与任务相关的工作流程及信息推荐而获益。本研究将工作流引擎中的人机交互模式从图形用户界面推进至软件工程中的对话式交互模式。