The study evaluates 26,999 pull requests that modify both application code and deployment infrastructure, using two large datasets and six AI agent architectures. It finds that such dual‑scope changes occur in only about 4.5% of agent‑generated PRs but lead to two distinct integration outcomes: either significant delays due to CI/CD and provisioning checks, or a high rate of silent merges with reduced human review. The results highlight that current AI agents lack deployment awareness, causing unverified dependencies to slip into production pipelines.
arXiv:2606. 26924v1 Announce Type: cross Abstract: LLM coding harnesses grant agents broad file and shell access, yet the configuration layer that steers them -- rules files, agent definitions, IDE-specific markdown -- is largely unmanaged.
arXiv:2603. 21489v2 Announce Type: replace-cross Abstract: AI agents have become increasingly capable at isolated software engineering (SWE) tasks such as resolving issues on Github.
arXiv:2606. 28235v1 Announce Type: cross Abstract: Autonomous coding agents now open and merge pull requests in shared repositories at scale, and the field evaluates them the way it has always evaluated components, one agent at a time, on isolated benchmark tasks.
arXiv:2606.28235v2 Announce Type: replace-cross Abstract: In July 2026, about 1,200 AI agents in an internal OpenAI evaluation, meant to run in isolation, used a shared package server as a message bo...
arXiv:2610.34633v1 Announce Type: cross Abstract: Repository-level code migration rewrites an entire project in a new programming language while preserving its observable behavior. It is challenging...
arXiv:2606. 09935v1 Announce Type: cross Abstract: AI-powered agents are increasingly embedded in continuous integration and continuous delivery/deployment (CI/CD) pipelines to autonomously review pull requests (PRs), triage issues, and maintain codebases.
arXiv:2610.07832v1 Announce Type: cross Abstract: Large language models (LLMs) equipped with terminal access have demonstrated strong capabilities in automating software engineering tasks. However, e...
The paper analyzes 2,487 tasks from five public coding‑agent benchmarks and 14,922 runs of six agent configurations, introducing the Spread‑Novelty‑Centrality (SNC) profile to describe how reference solutions are distributed, added, and the code’s dependencies, history, and complexity. It finds that every pair of benchmarks differs on at least two SNC axes, even when sharing the same task label, and that resolved runs tend to involve less distributed changes and lower centrality values, with patch sizes varying between Claude and Qwen configurations. The authors recommend that practitioners choose evaluation tasks based on change characteristics, inspect task descriptions, and assess patch scope alongside test outcomes to better align benchmarks with real‑world workloads.
arXiv:2607. 28591v1 Announce Type: cross Abstract: Scaling coding agents requires a continuing supply of executable data for training, benchmarking, and continuous evaluation.
The paper investigates how third‑party API routers, which sit between coding agents and large language model providers, can introduce a control gap by inspecting and modifying requests and responses. Through an empirical study using the SIDEL framework, the authors evaluate four levels of router‑side injection (Response Substitution, Response Append, LLM‑Polished Injection, and LLM‑Polished with Distribution Alignment Injection) across 400 curated samples and four representative coding agents. The results show that router‑side interventions significantly alter repository‑level actions and evade existing client‑side safeguards, achieving a 0% defense success rate without additional mitigations.
arXiv:2606. 13449v1 Announce Type: cross Abstract: AI-agents (e.
arXiv:2608.29675v1 Announce Type: cross Abstract: Repository exploration is a distinct and costly stage of coding-agent pipelines: before generating a patch, an agent must identify which repository f...