AI-assisted Programming May Decrease the Productivity of Experienced Developers by Increasing Maintenance Burden
- URL: http://arxiv.org/abs/2510.10165v2
- Date: Fri, 24 Oct 2025 00:51:19 GMT
- Title: AI-assisted Programming May Decrease the Productivity of Experienced Developers by Increasing Maintenance Burden
- Authors: Feiyang Xu, Poonacha K. Medappa, Murat M. Tunc, Martijn Vroegindeweij, Jan C. Fransoo,
- Abstract summary: Generative AI solutions like GitHub Copilot have been shown to increase the productivity of software developers.<n>We analyze developer activity in Open Source Software (OSS) projects following the introduction of GitHub Copilot.<n>We find that productivity indeed increases. However, the increase in productivity is primarily driven by less-experienced (peripheral) developers.
- Score: 1.074887112239958
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Generative AI solutions like GitHub Copilot have been shown to increase the productivity of software developers. Yet prior work remains unclear on the quality of code produced and the challenges of maintaining it in software projects. If quality declines as volume grows, experienced developers face increased workloads reviewing and reworking code from less-experienced contributors. We analyze developer activity in Open Source Software (OSS) projects following the introduction of GitHub Copilot. We find that productivity indeed increases. However, the increase in productivity is primarily driven by less-experienced (peripheral) developers. We also find that code written after the adoption of AI requires more rework. Importantly, the added rework burden falls on the more experienced (core) developers, who review 6.5% more code after Copilot's introduction, but show a 19% drop in their original code productivity. More broadly, this finding raises caution that productivity gains of AI may mask the growing burden of maintenance on a shrinking pool of experts.
Related papers
- Developer Productivity with GenAI [17.44738403505224]
We surveyed 415 software practitioners to capture their perceptions of productivity changes associated with AI-assisted development.<n>Results reveal limited overall productivity change, highlighting the productivity paradox in which developers become faster but do not necessarily create better software or feel more fulfilled.
arXiv Detail & Related papers (2025-10-28T10:23:57Z) - 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) - Echoes of AI: Investigating the Downstream Effects of AI Assistants on Software Maintainability [5.677464428950146]
This study investigates whether co-development with AI assistants affects software maintainability.<n> AI-assisted development in Phase 1 led to a modest speedup in subsequent evolution.<n>For habitual AI users, the mean speedup was 55.9%.
arXiv Detail & Related papers (2025-07-01T14:24:37Z) - Understanding Code Understandability Improvements in Code Reviews [79.16476505761582]
We analyzed 2,401 code review comments from Java open-source projects on GitHub.
83.9% of suggestions for improvement were accepted and integrated, with fewer than 1% later reverted.
arXiv Detail & Related papers (2024-10-29T12:21:23Z) - The Impact of Generative AI on Collaborative Open-Source Software Development: Evidence from GitHub Copilot [4.8256226973915455]
Using GitHub's proprietary Copilot usage data, we find that Copilot use increases project-level code contributions by 5.9%.<n>This gain is driven by a 2.1% increase in individual code contributions and a 3.4% rise in developer coding participation.<n>While AI expands who can contribute and how much they contribute, it slows coordination in collective development efforts.
arXiv Detail & Related papers (2024-10-02T23:26:10Z) - Does Co-Development with AI Assistants Lead to More Maintainable Code? A Registered Report [6.7428644467224]
This study aims to examine the influence of AI assistants on software maintainability.
In Phase 1, developers will add a new feature to a Java project, with or without the aid of an AI assistant.
In Phase 2, a randomized controlled trial, will involve a different set of developers evolving random Phase 1 projects - working without AI assistants.
arXiv Detail & Related papers (2024-08-20T11:48:42Z) - Transforming Software Development: Evaluating the Efficiency and Challenges of GitHub Copilot in Real-World Projects [0.0]
GitHub Copilot is an AI-powered coding assistant.
This study evaluates the efficiency gains, areas for improvement, and emerging challenges of using GitHub Copilot.
arXiv Detail & Related papers (2024-06-25T19:51:21Z) - Impact of the Availability of ChatGPT on Software Development: A Synthetic Difference in Differences Estimation using GitHub Data [49.1574468325115]
ChatGPT is an AI tool that enhances software production efficiency.
We estimate ChatGPT's effects on the number of git pushes, repositories, and unique developers per 100,000 people.
These results suggest that AI tools like ChatGPT can substantially boost developer productivity, though further analysis is needed to address potential downsides such as low quality code and privacy concerns.
arXiv Detail & Related papers (2024-06-16T19:11:15Z) - CONCORD: Clone-aware Contrastive Learning for Source Code [64.51161487524436]
Self-supervised pre-training has gained traction for learning generic code representations valuable for many downstream SE tasks.
We argue that it is also essential to factor in how developers code day-to-day for general-purpose representation learning.
In particular, we propose CONCORD, a self-supervised, contrastive learning strategy to place benign clones closer in the representation space while moving deviants further apart.
arXiv Detail & Related papers (2023-06-05T20:39:08Z) - The GitHub Development Workflow Automation Ecosystems [47.818229204130596]
Large-scale software development has become a highly collaborative endeavour.
This chapter explores the ecosystems of development bots and GitHub Actions.
It provides an extensive survey of the state-of-the-art in this domain.
arXiv Detail & Related papers (2023-05-08T15:24:23Z) - Generation Probabilities Are Not Enough: Uncertainty Highlighting in AI Code Completions [54.55334589363247]
We study whether conveying information about uncertainty enables programmers to more quickly and accurately produce code.
We find that highlighting tokens with the highest predicted likelihood of being edited leads to faster task completion and more targeted edits.
arXiv Detail & Related papers (2023-02-14T18:43:34Z)
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.