所有权的普遍性--所有权管理中的问题与挑战
Ownership at Large -- Open Problems and Challenges in Ownership Management
摘要：
软件密集型组织依赖于大量不同类型的软件资产，例如，源代码文件、数据仓库中的表和软件配置。谁是给定资产的最合适的所有者随时间而变化，例如，由于重组和个别职能的变化。新形式的自动化可以帮助在给定的时间点为任何给定的资产建议更合适的所有者。通过在所有权健康方面作出这种努力，所有权的问责制得到加强。为资产找到最合适的所有者的问题本质上是一个程序理解问题：我们如何自动确定谁最适合理解、维护、发展（从而承担所有权）给定的资产。本文介绍了Facebook Ownesty系统，该系统使用超大规模数据采掘和机器学习的组合，并已部署在Facebook的公司的所有权管理方法的一部分。Ownesty处理数百万的软件资产（例如，源代码文件），并考虑到工作流和组织方面。本文阐述了开放的问题和挑战的所有权的研究社区与预期的进步，从软件工程，程序设计语言和机器学习领域。
英文摘要：
Software-intensive organizations rely on large numbers of software assets of different types, e.g., source-code files, tables in the data warehouse, and software configurations. Who is the most suitable owner of a given asset changes over time, e.g., due to reorganization and individual function changes. New forms of automation can help suggest more suitable owners for any given asset at a given point in time. By such efforts on ownership health, accountability of ownership is increased. The problem of finding the most suitable owners for an asset is essentially a program comprehension problem: how do we automatically determine who would be best placed to understand, maintain, evolve (and thereby assume ownership of) a given asset. This paper introduces the Facebook Ownesty system, which uses a combination of ultra large scale data mining and machine learning and has been deployed at Facebook as part of the company's ownership management approach. Ownesty processes many millions of software assets (e.g., source-code files) and it takes into account workflow and organizational aspects. The paper sets out open problems and challenges on ownership for the research community with advances expected from the fields of software engineering, programming languages, and machine learning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
