Introduces Knowledge-Based Pull Requests as a workflow that separates knowledge acceptance from code merge using agent distillation and project-side regeneration.
Where do ai coding agents fail? an empirical study of failed agentic pull requests in github
9 Pith papers cite this work. Polarity classification is still indexing.
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AI coding agent adoption causes no change in human contributor count but reduces contributor density and newcomer share by 3.7pp while increasing review depth by 5.3% in a staggered DiD analysis of 11k GitHub projects.
Observational study of 20,574 sessions identifies seven misalignment forms where 90.5% cause effort/trust costs and 91.5% require explicit user correction, varying by interface and over time.
Large-scale trajectory analysis of 19 coding agents on 500 tasks finds that LLM choice drives outcomes more than framework design and that context-gathering plus validation behaviors improve success beyond task difficulty predictions.
Review is the control point for AI-authored code: teams set the sign of AI’s effect through reviewer expertise and process design, not the technology itself.
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.
PAFT improves LLM-based program repair pass rates by up to 65.6% while cutting average edit distance by up to 32.6% through explicit preservation signals and curriculum training.
ProjectMem implements a local event-sourced memory and judgment layer for AI coding agents that logs typed events, projects them to MCP summaries, and applies deterministic pre-action gates to avoid known failures.
Proposes a five-stage agentic AI framework for code review with human quality gates to maintain context, accountability, and team understanding.
citing papers explorer
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Knowledge-Based Pull Requests: A Trusted Workflow for Agent-Mediated Knowledge Collaboration
Introduces Knowledge-Based Pull Requests as a workflow that separates knowledge acceptance from code merge using agent distillation and project-side regeneration.
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Augmentation with Dilution: A Large-Scale Empirical Study of Human Contributor Ecosystems After AI Coding Agent Adoption
AI coding agent adoption causes no change in human contributor count but reduces contributor density and newcomer share by 3.7pp while increasing review depth by 5.3% in a staggered DiD analysis of 11k GitHub projects.
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How Coding Agents Fail Their Users: A Large-Scale Analysis of Developer-Agent Misalignment in 20,574 Real-World Sessions
Observational study of 20,574 sessions identifies seven misalignment forms where 90.5% cause effort/trust costs and 91.5% require explicit user correction, varying by interface and over time.
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Beyond Resolution Rates: Behavioral Drivers of Coding Agent Success and Failure
Large-scale trajectory analysis of 19 coding agents on 500 tasks finds that LLM choice drives outcomes more than framework design and that context-gathering plus validation behaviors improve success beyond task difficulty predictions.
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3100 Opinions on Code Review in an AI World: Building Causal Theory from Practitioner Discourse
Review is the control point for AI-authored code: teams set the sign of AI’s effect through reviewer expertise and process design, not the technology itself.
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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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PAFT: Preservation Aware Fine-Tuning for Minimal-Edit Program Repair
PAFT improves LLM-based program repair pass rates by up to 65.6% while cutting average edit distance by up to 32.6% through explicit preservation signals and curriculum training.
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PROJECTMEM: A Local-First, Event-Sourced Memory and Judgment Layer for AI Coding Agents
ProjectMem implements a local event-sourced memory and judgment layer for AI coding agents that logs typed events, projects them to MCP summaries, and applies deterministic pre-action gates to avoid known failures.
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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.