A publicly released dataset of 15,591 configuration artifacts for five agentic AI coding tools, drawn from 4,738 GitHub repositories along with associated files and AI-co-authored commits.
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On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHub
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An empirical taxonomy of 11 top-level categories and 27 subcategories of runtime faults in MCP servers, derived via open coding of GitHub threads and validated by a survey of 55 developers.
AI agents modify logging less often than humans in 58.4% of repositories but produce higher log density when they change it; explicit logging instructions are rare (4.7%) and ignored 67% of the time, with humans performing 72.5% of post-generation log repairs.
AgenticFlict is a public dataset of 29K+ textual merge conflicts from AI agent PRs, collected via merge simulation on 107K processed PRs and showing a 27.67% conflict rate with variation across agents.
AI coding agents produce pull requests with substantially more commits and slightly higher description-to-diff similarity than human developers, based on analysis of 29,095 merged PRs.
RepoRescue creates a benchmark of 315 repositories and shows LLM agents rescue up to 41.5% with runtime enforcement and 62.7% when combining systems, with hardest cases requiring cross-file changes.
An empirical study of 86,156 test patches from five AI agents finds 80.2% lack strong oracle signals, with strong oracles linked to higher merge rates (OR=1.28) after regression controls.
Qualitative analysis of 306 rejected AI-generated PRs reveals 14 rejection reasons in four categories, highlighting needs for better guidance on implementation, validation, and prioritization.
Exploratory interview study with 17 developers identifies four forms of emergent oversight work for software agents and documents situated challenges and heuristics.
Analysis of 9,799 human-reviewed agentic PRs shows only 35.7% of rejections reflect clear agent failures, with 31.2% due to workflow constraints and 33.1% lacking clear rationale, plus notable interaction differences across agents.
AI-generated code requires less maintenance than human-written code, mostly involving feature additions by humans rather than bug fixes.
Hot fixes show urgency patterns with reduced collaboration and testing, differing from regular fixes, and human versus AI agents display over 10 distinct repair behaviors in large-scale GitHub data.
Reviewer bots' higher comment volume on AI agent PRs is associated with slower resolutions and poorer average feedback quality, while feedback quality itself has no association with PR outcomes.
AI-generated security pull requests frequently contain a small set of recurring weaknesses, with many flawed ones merged and rejections driven by process factors rather than technical issues.
AI coding assistants introduce code issues that persist in 22.7% of cases across real projects, creating measurable long-term technical debt.
Code review agents achieve 45.20% merge rate on PRs versus 68.37% for humans, with 60.2% of agent-only closed PRs showing 0-30% signal quality.
Agentic Agile-V uses Agile-V as backbone and a Specify-Constrain-Orchestrate-Prove-Evolve-Verify loop to convert AI agent conversations into traceable engineering artifacts with acceptance evidence.
Proposes a five-stage agentic AI framework for code review with human quality gates to maintain context, accountability, and team understanding.
Qualitative interview study of 16 practitioners finds most companies at Levels 1-2 of agentic AI maturity and identifies a capability-deployment verification gap as the core barrier to production use.
citing papers explorer
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A Dataset of Agentic AI Coding Tool Configurations
A publicly released dataset of 15,591 configuration artifacts for five agentic AI coding tools, drawn from 4,738 GitHub repositories along with associated files and AI-co-authored commits.
-
A Taxonomy of Runtime Faults in Model Context Protocol Servers
An empirical taxonomy of 11 top-level categories and 27 subcategories of runtime faults in MCP servers, derived via open coding of GitHub threads and validated by a survey of 55 developers.
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Do AI Coding Agents Log Like Humans? An Empirical Study
AI agents modify logging less often than humans in 58.4% of repositories but produce higher log density when they change it; explicit logging instructions are rare (4.7%) and ignored 67% of the time, with humans performing 72.5% of post-generation log repairs.
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AgenticFlict: A Large-Scale Dataset of Merge Conflicts in AI Coding Agent Pull Requests on GitHub
AgenticFlict is a public dataset of 29K+ textual merge conflicts from AI agent PRs, collected via merge simulation on 107K processed PRs and showing a 27.67% conflict rate with variation across agents.
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How AI Coding Agents Modify Code: A Large-Scale Study of GitHub Pull Requests
AI coding agents produce pull requests with substantially more commits and slightly higher description-to-diff similarity than human developers, based on analysis of 29,095 merged PRs.
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RepoRescue: An Empirical Study of LLM Agents on Whole-Repository Compatibility Rescue
RepoRescue creates a benchmark of 315 repositories and shows LLM agents rescue up to 41.5% with runtime enforcement and 62.7% when combining systems, with hardest cases requiring cross-file changes.
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All Smoke, No Alarm: Oracle Signals in Agent-Authored Test Code
An empirical study of 86,156 test patches from five AI agents finds 80.2% lack strong oracle signals, with strong oracles linked to higher merge rates (OR=1.28) after regression controls.
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Understanding the Rejection of Fixes Generated by Agentic Pull Requests -- Insights from the AIDev Dataset
Qualitative analysis of 306 rejected AI-generated PRs reveals 14 rejection reasons in four categories, highlighting needs for better guidance on implementation, validation, and prioritization.
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Human oversight of agentic systems in practice: Examining the oversight work, challenges, and heuristics of developers using software agents
Exploratory interview study with 17 developers identifies four forms of emergent oversight work for software agents and documents situated challenges and heuristics.
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Why Are Agentic Pull Requests Merged or Rejected? An Empirical Study
Analysis of 9,799 human-reviewed agentic PRs shows only 35.7% of rejections reflect clear agent failures, with 31.2% due to workflow constraints and 33.1% lacking clear rationale, plus notable interaction differences across agents.
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To What Extent Does Agent-generated Code Require Maintenance? An Empirical Study
AI-generated code requires less maintenance than human-written code, mostly involving feature additions by humans rather than bug fixes.
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Hot Fixing in the Wild
Hot fixes show urgency patterns with reduced collaboration and testing, differing from regular fixes, and human versus AI agents display over 10 distinct repair behaviors in large-scale GitHub data.
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On the Footprints of Reviewer Bots Feedback on Agentic Pull Requests in OSS GitHub Repositories
Reviewer bots' higher comment volume on AI agent PRs is associated with slower resolutions and poorer average feedback quality, while feedback quality itself has no association with PR outcomes.
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Insights into Security-Related AI-Generated Pull Requests
AI-generated security pull requests frequently contain a small set of recurring weaknesses, with many flawed ones merged and rejections driven by process factors rather than technical issues.
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Debt Behind the AI Boom: A Large-Scale Empirical Study of AI-Generated Code in the Wild
AI coding assistants introduce code issues that persist in 22.7% of cases across real projects, creating measurable long-term technical debt.
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From Industry Claims to Empirical Reality: An Empirical Study of Code Review Agents in Pull Requests
Code review agents achieve 45.20% merge rate on PRs versus 68.37% for humans, with 60.2% of agent-only closed PRs showing 0-30% signal quality.
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Agentic Agile-V: From Vibe Coding to Verified Engineering in Software and Hardware Development
Agentic Agile-V uses Agile-V as backbone and a Specify-Constrain-Orchestrate-Prove-Evolve-Verify loop to convert AI agent conversations into traceable engineering artifacts with acceptance evidence.
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Rethinking Code Review in the Age of AI: A Vision for Agentic Code Review
Proposes a five-stage agentic AI framework for code review with human quality gates to maintain context, accountability, and team understanding.
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Agentic AI in Industry: Adoption Level and Deployment Barriers
Qualitative interview study of 16 practitioners finds most companies at Levels 1-2 of agentic AI maturity and identifies a capability-deployment verification gap as the core barrier to production use.