计算机科学
代码库
编码(集合论)
数据库
一致性(知识库)
PB级
源代码行
软件
资产(计算机安全)
可靠性(半导体)
软件工程
操作系统
程序设计语言
计算机安全
集合(抽象数据类型)
功率(物理)
大数据
物理
量子力学
人工智能
作者
Will Shackleton,Katriel Cohn-Gordon,Peter C. Rigby,Rui Abreu,James Gill,Nachiappan Nagappan,Karim Nakad,Ioannis Papagiannis,Luke Petre,Giorgi Megreli,Patrick Riggs,James Saindon
标识
DOI:10.1145/3611643.3613871
摘要
Software constantly evolves in response to user needs: new features are built, deployed, mature and grow old, and eventually their usage drops enough to merit switching them off. In any large codebase, this feature lifecycle can naturally lead to retaining unnecessary code and data. Removing these respects users' privacy expectations, as well as helping engineers to work efficiently. In prior software engineering research, we have found little evidence of code deprecation or dead-code removal at industrial scale. We describe Systematic Code and Asset Removal Framework (SCARF), a product deprecation system to assist engineers working in large codebases. SCARF identifies unused code and data assets and safely removes them. It operates fully automatically, including committing code and dropping database tables. It also gathers developer input where it cannot take automated actions, leading to further removals. Dead code removal increases the quality and consistency of large codebases, aids with knowledge management and improves reliability. SCARF has had an important impact at Meta. In the last year alone, it has removed petabytes of data across 12.8 million distinct assets, and deleted over 104 million lines of code.
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