Orchestrating AI-Assisted Code Remediation: Socio-Technical Bottlenecks in a Large Industrial Repository

A 15-day field study inside a large, closed-source industrial C++ repository found that once a developer used a command-line AI coding assistant to remediate widespread code issues at scale, editing was never the constraint. Per-file commits from the agent were numerous enough to saturate the team's build-on-commit continuous integration and overwhelm reviewer attention; switching to directory-based batching and capping the number of files per change restored throughput, but still required explicit negotiation with reviewers over what counted as an acceptable commit. The authors, drawing on Gerrit metadata plus a developer diary and team chat, conclude that a semantic change set, such as "fix all instances of warning X," needs to be treated as a first-class unit of work that gets sliced differently for the developer, the reviewer and CI. This account reflects the paper's own abstract; Dark Factory has not yet reviewed the full study.

Why it matters: The bottleneck this study found sits downstream of the model, in CI and review capacity, which is exactly the constraint a harness or workflow needs to plan around before turning an agent loose on a large remediation job.