发表机构
Ericsson AB; Lund University; Mälardalen University; Carleton University（爱立信; 隆德大学; 马勒尔谷大学; 卡尔顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过15天工业案例，发现AI辅助代码修复中CI容量、审查精力和变更编排是主要瓶颈，需控制提交与批处理粒度并视语义变更集为一等工作单元。
AI 中文摘要
背景：大型、长期存在的代码库中的代码退化通过手动重构和机会性清理来修复成本高昂。基于LLM的编码助手可以在大规模范围内执行机械性修复，但它们对工业工作流程的影响尚未得到充分探索。目标：我们研究大规模AI辅助代码修复如何影响大型工业代码库中的提交时构建持续集成（CI）、代码审查和团队协调，以及当通过AI辅助使源代码编辑变得廉价时，哪些社会技术瓶颈会制约此类修复。方法：我们报告了一项为期15天的探索性单案例现场研究，其中一位经验丰富的开发人员使用命令行AI编码助手来修复一个闭源工业C++代码库中的广泛问题。我们将Gerrit元数据与开发人员日记和团队聊天进行三角验证，并通过描述性统计和定性编码进行分析。结果：AI辅助修复迅速生成了数百个涉及数千行代码的提交，使CI和审查者的注意力饱和。天真的逐文件提交使提交时构建CI过载；切换到基于目录的批处理并限制每次更改的文件数量恢复了吞吐量，但仍然需要明确的审查请求、对可接受提交粒度的协商，以及迭代跟进以解决构建和静态分析失败。结论：当机械编辑变得廉价时，CI容量、审查工作和更改编排成为主要瓶颈。在非常大的代码库中，可持续的AI辅助修复需要刻意控制提交、审查和CI批处理粒度，并将语义更改集（例如“修复警告X的所有实例”）视为一等工作单元，这些单元可以针对开发人员、审查者和CI进行不同切片。
英文摘要
Background: Code degradation in large, long-lived codebases is costly to remediate through manual refactoring and opportunistic clean-ups. LLM-based coding assistants can perform mechanical remediation at scale, but their impact on industrial workflows is underexplored. Objective: We investigate how massive AI-assisted code remediation affects build-on-commit continuous integration (CI), code review, and team coordination in a large industrial repository, and which socio-technical bottlenecks constrain such remediation when source editing becomes cheap through AI assistance. Method: We report on a 15-day exploratory single-case field study in which an experienced developer used a command-line AI coding buddy to remediate widespread issues in a closed-source industrial C++ repository. We triangulate Gerrit metadata with a developer diary and team chat, analyzed through descriptive statistics and qualitative coding. Results: AI-assisted remediation rapidly generated hundreds of commits touching thousands of lines, saturating CI and reviewer attention. Naïve per-file commits overloaded build-on-commit CI; Switching to directory-based batching and capping the number of files per change restored throughput, but still required explicit review solicitation, negotiation of acceptable commit granularity, and iterative follow-up to resolve build and static-analysis failures. Conclusion: When mechanical editing is cheap, CI capacity, review effort, and change orchestration become primary bottlenecks. Sustainable AI-assisted remediation in very large repositories requires deliberate control of commit, review, and CI batch granularity and treating semantic change sets, such as ``fix all instances of warning X'', as first-class units of work that can be sliced differently for developers, reviewers, and CI.
CommentsSubmitted
