Jul 2026· AIware· pp. 188-196· 2 citations· 42 references
Computer Science
TL;DR
Large-scale human–agent collaboration is investigated by comparing 40,214 pull requests across 2,807 GitHub repositories, including 33,596 agent-authored PRs from five autonomous coding agents and 6,618 human-authored PRs, revealing a socio-technical trade-off.
Abstract
Autonomous coding agents are increasingly participating in collaborative software development by generating repository-level pull requests (PRs) that must be reviewed and integrated by human teams. While prior work has examined the technical characteristics of agent-generated patches, less is known about how autonomous authorship reshapes human collaboration dynamics in real-world workflows. In this paper, we investigate large-scale human–agent collaboration by comparing 40,214 pull requests across 2,807 GitHub repositories, including 33,596 agent-authored PRs from five autonomous coding agents and 6,618 human-authored PRs. We examine differences across three dimensions: integration outcomes, structural characteristics, and collaboration signals. Our findings reveal a socio-technical trade-off. Agent-authored PRs are integrated significantly faster, yet exhibit lower merge rates overall. Task type moderates this effect: agents outperform humans in documentation tasks but underperform in behavior-changing contributions. Beyond outcomes, collaboration patterns differ systematically. Agent-authored PRs attract proportionally more bot-generated comments and elicit more analytic, less socially oriented review communication. In contrast, human-authored PRs receive more elaborative and socially engaged feedback. Incorporating psycholinguistic features into predictive models significantly improves merge outcome prediction, demonstrating that communication style carries explanatory power beyond structural code characteristics. These results suggest that autonomous agents do not merely introduce technical artifacts into repositories, but actively reshape review interaction patterns and evaluative behavior. The impact of coding agents is therefore fundamentally socio-technical, highlighting the importance of studying AI systems within authentic human–AI collaborative environments.
A descriptive model of agent-documentation interaction is derived as a two-lobed cycle rather than a linear journey, and it is shown that two widely assumed properties of"agent-friendly"documentation - actionability and verifiability - lack consistent behavioural support.
This study characterize agentic pull requests (PR) in comparison to human generated PRs and to examine how their properties change across different stages of the development lifecycle, offering a more nuanced understanding of their benefits and limitations in real-world practices.
Iren Mazloomzadeh, Mohammad Mehdi Morovati, F. Khomh· 0 citations
Analysis of agentic PRs from popular GitHub repositories provides early empirical evidence on how open-source projects organize human oversight around agentic coding tools and suggests that successful integration of agent-generated contributions depends not only on advances in agent capabilities but also on the human and organizational processes that govern their use.
AI agents are joining human teams, raising a basic question: when an automated agent becomes a regular participant, does group organization strengthen or weaken? We study this question in open-source software, where bots open pull requests, review code, and merge changes alongside people, leaving a public record of every interaction. Treating bots as participants rather than tools, we examine 2,991 GitHub projects for two years before and after each adopted its first bot. We measure three capabilities that institutional theory links to durable coordination - repeated engagement, social memory, and role differentiation - and two outcomes: conflict cascades and output distinctiveness. Bot adoption is followed by more repeated collaboration, greater recognition of specific bots in discussion, fewer conflict cascades, and more distinctive outputs. These changes cluster around adoption rather than accumulating gradually. Because we lack an untreated comparison group, we interpret the results as precisely timed associations, not causal effects. Two patterns are difficult for alternative explanations to account for: capabilities predict outcomes according to their function - coordination versus differentiation - rather than whether humans or bots provide them, and human-side capabilities account for the bot-conflict association but not the bot-distinctiveness association. The findings are consistent with a specific interpretation: predictable, rule-based agents can become part of a community's social infrastructure. The bot is the occasion; social organization is the mechanism.
Emerging research in computational social science has applied LLMs to automate content analysis, often by prompting a single model to act as a human coder. While a single LLM may suffice for a few manifest variables, it still falls short on diverse latent constructs. And the impact of LLM agent attributes on measurement outcomes remains unclear, limiting their validity for communication research. Drawing upon the literature on interacting agents and communication, this study examines the impact of agent diversity, agent open-mindedness, and human–AI collaboration (HAIC) in a multi-LLM-agent system for automated content analysis. The results demonstrate reliable and accurate measurement of four communication variables across three datasets, with improved performance following agent discussion. Additionally, agent open-mindedness, but not agent diversity, significantly affects measurement outcomes. These results highlight the potential of multi-LLM-agent systems for automated content analysis and suggest the importance of considering agent attributes and values in system design.
Xinyan Zhao, Chengshuai Zhao, Mordecai Mengesteab et al.· Social science computer revi...· 0 citations
AI coding agents may generate and submit Pull Requests (PRs) to the same repository at the same time. However, research concerning the extent of concurrent submission by AI coding agents to a common repository does not exist. This paper uses the AIDev-pop dataset (33,596 PRs in 2,807 repositories) to provide the first empirical examination of the prevalence of concurrent submission using PRs authored by agents. We report that when considering exact temporal overlap, 40.2% of repositories contain co-active agent-authored PR pairs; further, the co-active pairs account for 79.4% of all PRs generated by an AI agent. When we examine co-activity within a one week collaboration window, the percentages are increased to 53.4% and 95.0%, respectively. For the majority of the co-active PR pairs (underlying the vast majority of which are intra-agent authored), both PRs were authored by the same agent, while only 0.5% of co-active pairs were cross-agent, and occurred in only 122 out of 2807 total repositories examined (or approximately 4.3%). Additionally, we replayed actual three way git merges on 747 unique co-active pairs (one per repository), and computed the percentage of textual conflict encountered during the merge operation to combine the two PRs in each pair. We observed that the percentage of textual conflict encountered was significantly higher for cross-agent pairs compared to intra-agent pairs: 41.7% vs. 19.8%, respectively, with non-overlapping 95% confidence intervals. Lastly, we developed a classification system based on the detection of conflict reported by git, and determined that the majority of conflicts resulted from modifications to source code files (84.4% of conflicted files) and not dependency manifest files; further, nearly 42% of conflicts we observed were structural (i.e., modify/delete or add/add).
George Xu, Arjun Subramanian, N. Karthik· 0 citations