SHF: Small: Collaborative Research:Discerning and Recommending Context-Specific Best Practices in DevOps-Oriented Software Development

这个项目是对现代软件开发实践的科学研究，也就是众所周知的DevOps。DevOps文化主要是通过自动化构建、测试和部署软件的过程，在不影响软件质量的情况下，尽可能快地将变更引入软件生产。在实践中，DevOps工程师可以在众多工具中进行选择，包括配置管理、基于云的持续集成和自动部署。在使用单个工具时，通常没有多少关于如何适应全局的指导，关于最佳实践的问题在在线论坛上比比皆是。然而，现有的答案通常是一般的经验法则或过时的建议，主要基于第三方的经验，通常不适用于特定的上下文。事实上，目前关于DevOps实践有效性的经验证据非常分散和不完整。最先进的决策支持，基于硬数据和明智的建议，可以帮助DevOps工程师为他们的任务识别最佳选择和实践。提出的研究以权变理论为基础，在推理实践的有效性时强调任务背景。这个项目的目标是通过挖掘和分析来自协作编码平台GitHub的数据，学习和传达DevOps环境中最佳实践的结构化、上下文相关的分析。使用已建立的和新颖的定性和定量技术，本研究将：(1)识别具有相似上下文变量的软件项目集群；(2)在感兴趣的上下文中，辨别DevOps实践（如持续集成）最有效（和最无效）的条件。这将为DevOps团队提供可操作的知识和工具支持，为他们的环境定制有效的项目实践，并推进软件工程的理论和实践，特别是与分布式、快节奏、自动化繁重的环境相关的理论和实践。

This project is a scientific study of modern software development practices, which has become known as DevOps. The DevOps culture seeks to bring changes into software production as quickly as possible without compromising software quality, primarily by automating the processes of building, testing, and deploying software. In practice, DevOps engineers can choose between a multitude of tools, including configuration management, cloud-based continuous integration, and automated deployment. Often individual tools are used without much guidance on how they fit in the big picture, and questions about best practices abound in online forums. However, existing answers are typically generic rules of thumb or dated advice, mostly based on third-party experiences, often non-applicable to the specific context. In fact, current empirical evidence on the effectiveness of DevOps practices is much fragmented and incomplete. State-of-the-art decision-making support, based on hard data and informed advice, can help DevOps engineers discern the best choices and practices for their tasks. The proposed research is grounded in contingency theory, where the emphasis is on task context when reasoning about the effectiveness of practices. The goal of this project is to learn and convey structured, context-dependent analytics on best practices in DevOps environments, by mining and analyzing data from the collaborative coding platform GitHub. Using established and novel qualitative and quantitative techniques, this research will: (1) identify clusters of software projects that share similar context variables; and (2) within a context of interest, discern the conditions under which DevOps practices such as continuous integration are most (and least) effective. This will result in actionable knowledge and tool support for DevOps teams, to customize efficient project practices to their environment, as well as advance the theory and practice of software engineering, especially as it relates to distributed, fast paced, automation-heavy environments.