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文化寻求在不影响软件质量的情况下尽可能快地为软件生产带来变化，主要是通过自动化构建、测试和部署软件的过程。在实践中，DevOps工程师可以在多种工具中进行选择，包括配置管理、基于云的持续集成和自动化部署。通常，个别工具的使用没有太多关于它们如何适应整体情况的指导，关于最佳实践的问题在在线论坛中比比皆是。然而，现有的答案通常是一般性的经验法则或过时的建议，大多基于第三方的经验，往往不适用于特定的背景。事实上，目前关于DevOps实践有效性的经验证据非常零散和不完整。最先进的决策支持，基于硬数据和知情建议，可以帮助DevOps工程师识别出适合他们任务的最佳选择和实践。拟议的研究基于权变理论，在对实践的有效性进行推理时，重点是任务背景。该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，了解并传达对DevOps环境中最佳实践的结构化、上下文相关分析。使用已建立的和新的定性和定量技术，本研究将：(1)确定共享相似上下文变量的软件项目集群；以及(2)在感兴趣的上下文中，辨别诸如持续集成等DevOps实践最有效(和最不有效)的条件。这将为DevOps团队带来可操作的知识和工具支持，为他们的环境定制高效的项目实践，并推动软件工程的理论和实践，特别是与分布式、快节奏、自动化密集型环境有关的软件工程理论和实践。

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.