The End of Code Review: Coding Agents Supersede Human Inspection
- URL: http://arxiv.org/abs/2606.13175v1
- Date: Thu, 11 Jun 2026 10:43:48 GMT
- Title: The End of Code Review: Coding Agents Supersede Human Inspection
- Abstract summary: We argue that coding agents have crossed a threshold of capability at which traditional human code review is no longer a necessary component of a software quality pipeline.<n>Our argument rests on two claims: every stated goal of code review can be served by agents at lower cost and higher throughput.
- Score: 7.017303286271101
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Code review has been the primary quality gate in software development since Fagan formalised code inspection in 1976. For five decades, having a human examine and comment on a colleague's changes before merge has been a cornerstone practice at organisations of every size. Coding agents are large language model (LLM)-based autonomous systems capable of reading, writing, testing, and repairing software. We argue that coding agents have crossed a threshold of capability at which traditional human code review is no longer a necessary component of a software quality pipeline. Our argument rests on two claims: every stated goal of code review can be served by agents at lower cost and higher throughput; the naive integration in which agents write code and humans remain the mandatory reviewers is a dead end because it neither provides meaningful assurance nor scales with AI-assisted throughput.
Related papers
- Is Agent Code Less Maintainable Than Human Code? [17.226150835020146]
We investigate how agent code compares to human code in maintenance settings.<n>We find that agents are less effective at resolving tasks when building on agent code compared to human code.
arXiv Detail & Related papers (2026-06-19T23:56:14Z) - Code as Agent Harness [107.31925305395957]
In emerging agentic systems, code is no longer only a target output.<n>Code increasingly serves as an operational substrate for agent reasoning, acting, environment modeling, and execution-based verification.<n>This survey provides a unified roadmap toward executable, verifiable, and stateful AI agent systems.
arXiv Detail & Related papers (2026-05-18T17:59:03Z) - Rethinking Code Review in the Age of AI: A Vision for Agentic Code Review [2.2807488730605296]
We present a vision for an AI-powered code review workflow combining specialized agents with human-controlled quality.<n>We identify major open challenges for responsible adoption, including reliability, bias, privacy, automation bias, transparency, and evaluation.
arXiv Detail & Related papers (2026-05-17T17:04:21Z) - SWE-chat: Coding Agent Interactions From Real Users in the Wild [70.18158706281724]
SWE-chat is the first large-scale dataset of real coding agent sessions collected from open-source developers in the wild.<n>The dataset currently contains 6,000 sessions, comprising more than 63,000 user prompts and 355,000 agent tool calls.
arXiv Detail & Related papers (2026-04-22T17:08:19Z) - SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks [55.76734816061826]
We introduce SlopCodeBench, a language-agnostic benchmark comprising 20 problems and 93 checkpoints.<n>We track two trajectory-level quality signals: verbosity, the fraction of redundant or duplicated code, and structural erosion.<n>No agent solves any problem end-to-end across 11 models; the highest checkpoint solve rate is 17.2%.
arXiv Detail & Related papers (2026-03-25T19:26:44Z) - Code Review Agent Benchmark [11.281773600529212]
We curate a code review dataset for AI agents to work with.<n>Our evaluation framework can asses the reviewing capability of the code review agents.<n>What this will mean for future collaboration of code generation agents, test generation agents and code review agents -- remains to be investigated.
arXiv Detail & Related papers (2026-03-24T17:19:32Z) - Human-AI Synergy in Agentic Code Review [3.7086626614863984]
We compare the feedback differences provided by human reviewers and AI agents.<n>Human reviewers exchange 11.8% more rounds when reviewing AI-generated code than human-written code.<n>Over half of unadopted suggestions from AI agents are either incorrect or addressed through alternative fixes by developers.
arXiv Detail & Related papers (2026-03-16T20:56:18Z) - CodeClash: Benchmarking Goal-Oriented Software Engineering [63.65464283837602]
We run 1680 tournaments (25,200 rounds total) to evaluate 8 LMs across 6 arenas.<n>Our results reveal that while models exhibit diverse development styles, they share fundamental limitations in strategic reasoning.<n>We open-source CodeClash to advance the study of autonomous, goal-oriented code development.
arXiv Detail & Related papers (2025-11-02T07:42:51Z) - Code with Me or for Me? How Increasing AI Automation Transforms Developer Workflows [60.04362496037186]
We present the first controlled study of developer interactions with coding agents.<n>We evaluate two leading copilot and agentic coding assistants.<n>Our results show agents can assist developers in ways that surpass copilots.
arXiv Detail & Related papers (2025-07-10T20:12:54Z) - Codev-Bench: How Do LLMs Understand Developer-Centric Code Completion? [60.84912551069379]
We present the Code-Development Benchmark (Codev-Bench), a fine-grained, real-world, repository-level, and developer-centric evaluation framework.
Codev-Agent is an agent-based system that automates repository crawling, constructs execution environments, extracts dynamic calling chains from existing unit tests, and generates new test samples to avoid data leakage.
arXiv Detail & Related papers (2024-10-02T09:11:10Z) - CodeAgent: Autonomous Communicative Agents for Code Review [12.163258651539236]
This work introduces tool, a novel multi-agent Large Language Model (LLM) system for code review automation.
CodeAgent incorporates a supervisory agent, QA-Checker, to ensure that all the agents' contributions address the initial review question.
Results demonstrate CodeAgent's effectiveness, contributing to a new state-of-the-art in code review automation.
arXiv Detail & Related papers (2024-02-03T14:43:14Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.