Dependency update routing cuts repair agent calls in half
When Should Dependency Updates Invoke Repair Agents? A Lightweight Routing Study
Software Engineering
Summary
Updating software pieces called dependencies usually goes smoothly, but sometimes fixes are needed, which can be costly to check every time. The authors created a simple system, DepFixRouter, that predicts which updates might need a repair based on information available when the update is made. This helps save time and computer resources by calling repair tools only when really necessary. Tests showed it can reduce unnecessary checks by about two-thirds while still catching more than half of the problems early on.
What this means in practice
- •For software maintenance teams: Reduce time and resource waste by routing dependency updates to repair agents only when likely needed, improving maintenance efficiency.
- •For continuous integration operators: Cut down unnecessary continuous integration (CI) runs triggered by routine dependency updates by predicting which need repair agent intervention.
Authors
Liheng Fan, Jialun Yin, Yuzhi Chen
Abstract
Dependency-update pull requests are frequent and mostly routine, but a small subset requires non-trivial compatibility repair. Recent repository-level coding agents make such repair increasingly plausible, yet invoking them on every dependency update wastes model calls, CI time, repository context, and review attention. We frame this as a pre-agent routing problem: deciding which dependency-update pull requests should be escalated before downstream diagnosis or repair attempts. We introduce DepFixRouter, a lightweight router that ranks dependency updates by historical compatibility-repair likelihood using creation-time textual and metadata signals. On 497 labeled GitHub dependency-update candidates, only 72 require substantive repair. A creation-time-safe LinearSVC using only PR titles and bot/dependency flags reaches 0.488 repair F1 and captures 51.4% of repairs within the top 20% routed pull requests, improving calls per captured repair from 6.90 under route-all or random policies to 2.68. Retrospective full-history signals improve top-20% recall to 65.3%, revealing substantial hindsight leakage in pull-request histories rather than deployment-time routing utility. In a 60-case diagnosis-agent pilot, router-gated diagnosis reduces actual LLM calls by 66.7% and tokens by 66.1%, suggesting budgetaware escalation while measuring diagnosis rather than patch generation. DepFixRouter can serve as a lightweight escalation layer between routine dependency-update automation and expensive repository-level agents, enabling budget-aware maintenance without relying on retrospective repair evidence for deployment-time routing.