mlsanityData to 14 Oct 2026

Do These Violent Delights Have Violent Ends? Measuring the Post-Merge Fate of Agentic Code

arXiv 2607.09902 ↗First posted 10 Jul 2026cs.SE cs.AIcited by 0

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

Agentic coding tools are increasingly used to make autonomous repository-level changes to real-world projects. Prior work has largely evaluated these contributions at the pre-merge stage, through outcomes such as pull request acceptance and review effort. Far less is known about what happens to agentic code post-merge. Yet merge success alone does not reveal whether a contribution will remain stable or require bug fixes and other corrective maintenance downstream. We conduct a longitudinal empirical analysis of agentic and human contributions across 182 repositories, tracking their post-merge fate over time, characterizing the intent of subsequent modifications, and analyzing the defects and vulnerabilities they introduce. While the overall maintenance rates are similar, agentic contributions require significantly higher rates of corrective maintenance and introduce more security weaknesses and dependency vulnerabilities. We also find statistically significant evidence that agentic maintenance burden is associated with repository characteristics. In particular, each 10 percentage-point increase in a project's no-review rate is associated with roughly a 6% increase in agentic maintenance burden on average. As coding agents become pervasive in software development, our findings highlight the need to evaluate and design agentic tools not only to produce mergeable changes, but to produce contributions that remain secure and maintainable.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 32 of its 89 references that are in the map. First posted on arXiv 10 Jul 2026.

Builds on

32 of 89 references.

Closest ancestors · 11

Same direction, newest first

  1. 2026Why Are Agentic Pull Requests Merged or Rejected? An Empirical Study21 May
  2. 2026How AI Coding Agents Communicate: A Study of Pull Request Description Characteristics and Human Review Responses19 Feb
  3. 2026Why Agentic-PRs Get Rejected: A Comparative Study of Coding Agents4 Feb
  4. 2026Beyond Bug Fixes: An Empirical Investigation of Post-Merge Code Quality Issues in Agent-Generated Pull Requests27 Jan
  5. 2026Understanding Dominant Themes in Reviewing Agentic AI-authored Code27 Jan
  6. 2026AI IDEs or Autonomous Agents? Measuring the Impact of Coding Agents on Software Development20 Jan
  7. 2026Do Autonomous Agents Contribute Test Code? A Study of Tests in Agentic Pull Requests7 Jan
  8. 2026Early-Stage Prediction of Review Effort in AI-Generated Pull Requests2 Jan
  9. 2025Agentic Refactoring: An Empirical Study of AI Coding Agents6 Nov
  10. 2025Speed at the Cost of Quality: How Cursor AI Increases Short-Term Velocity and Long-Term Complexity in Open-Source Projects6 Nov
  11. 2025On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHub18 Sep

Other references · 21

  1. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  2. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
  3. 2021Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributionscited by 431
  4. 2023The Impact of AI on Developer Productivity: Evidence from GitHub Copilotcited by 274
  5. 2022Do Users Write More Insecure Code with AI Assistants?cited by 192
  6. 2025Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivitycited by 101
  7. 2022Productivity Assessment of Neural Code Completioncited by 98
  8. 2022Is GitHub's Copilot as Bad as Humans at Introducing Vulnerabilities in Code?cited by 67
  9. 2024How much does AI impact development speed? An enterprise-based randomized controlled trialcited by 20
  10. 2025Security and Quality in LLM-Generated Code: A Multi-Language, Multi-Model Analysiscited by 19
  11. 2024Transforming Software Development: Evaluating the Efficiency and Challenges of GitHub Copilot in Real-World Projectscited by 18
  12. 2024The Impact of Generative AI on Collaborative Open-Source Software Development: Evidence from GitHub Copilotcited by 11
9 more
  1. 2026Debt Behind the AI Boom: A Large-Scale Empirical Study of AI-Generated Code in the Wildcited by 10
  2. 2025The Effects of GitHub Copilot on Computing Students' Programming Effectiveness, Efficiency, and Processes in Brownfield Programming Taskscited by 9
  3. 2025Investigating The Smells of LLM Generated Codecited by 9
  4. 2025AI-Assisted Programming Decreases the Productivity of Experienced Developers by Increasing the Technical Debt and Maintenance Burdencited by 6
  5. 2024Impact of AI-tooling on the Engineering Workspacecited by 3
  6. 2024Harnessing the Potential of Gen-AI Coding Assistants in Public Sector Software Developmentcited by 3
  7. 2026Will It Survive? Deciphering the Fate of AI-Generated Code in Open Sourcecited by 3
  8. 2025Quality evaluation of Tabby coding assistant using real source code snippetscited by 2
  9. 2025"Maybe We Need Some More Examples:" Individual and Team Drivers of Developer GenAI Tool Usecited by 1

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