AIDev is a new open dataset of 456k AI-agent pull requests showing agents submit code faster than humans but with lower acceptance rates and simpler changes.
Agen- tic ai software engineer: Programming with trust
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RAG-Reflect achieves F1=0.78 on valid comment-edit prediction using retrieval-augmented reasoning and self-reflection, outperforming baselines and approaching fine-tuned models without retraining.
Study of 930k+ agent PRs shows repository explains ~50% of integration friction variance, with agents concentrating it twice as much as humans (ICC 0.30 vs 0.16) after controls.
A repository-level AGENTS.md governance framework with commit-level AI disclosure, session logging, pre-commit gates, and mandatory human review is demonstrated on two new TMAP8 validation cases.
AI coding tools divide into collaborators that initiate most PRs and assistants that support human-led ones, yet humans retain merge authority across all five tools examined.
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.
UntrustVul identifies untrustworthy vulnerability predictions by marking lines that neither match historical vulnerability patterns nor influence vulnerable lines through dependencies, reporting AUC 70-88% and F1 82-94% on 115K predictions.
Evaluates 42 variants of foundation models across three formalized paradigms for missing modality reconstruction, identifies shortfalls in semantic extraction and validation, and introduces an agentic framework that reduces FID by at least 14% for images and MER by at least 10% for text.
This interpretive synthesis maps three coexisting software engineering paradigms (Traditional, Generative AI-Enabled, Agentic AI-Enabled), proposes a five-category competency framework, and derives nine testable propositions about the shift toward human-AI collaboration.
A research proposal for three studies on multi-agent LLM pair programming that externalizes intent and uses automated validation to increase trustworthiness.
citing papers explorer
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The Rise of AI Teammates in Software Engineering (SE) 3.0: How Autonomous Coding Agents Are Reshaping Software Engineering
AIDev is a new open dataset of 456k AI-agent pull requests showing agents submit code faster than humans but with lower acceptance rates and simpler changes.
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RAG-Reflect: Agentic Retrieval-Augmented Generation with Reflections for Comment-Driven Code Maintenance on Stack Overflow
RAG-Reflect achieves F1=0.78 on valid comment-edit prediction using retrieval-augmented reasoning and self-reflection, outperforming baselines and approaching fine-tuned models without retraining.
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Govern the Repository, Not the Agent: Measuring Ecosystem-Level Risk in AI-Native Software
Study of 930k+ agent PRs shows repository explains ~50% of integration friction variance, with agents concentrating it twice as much as humans (ICC 0.30 vs 0.16) after controls.
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Bridging the Gap on AI-Assisted Scientific Software Development Through Transparency and Traceability
A repository-level AGENTS.md governance framework with commit-level AI disclosure, session logging, pre-commit gates, and mandatory human review is demonstrated on two new TMAP8 validation cases.
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Collaborator or Assistant? How AI Coding Agents Partition Work Across Pull Request Lifecycles
AI coding tools divide into collaborators that initiate most PRs and assistants that support human-led ones, yet humans retain merge authority across all five tools examined.
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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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UntrustVul: An Automated Approach for Identifying Untrustworthy Alerts in Vulnerability Detection Models
UntrustVul identifies untrustworthy vulnerability predictions by marking lines that neither match historical vulnerability patterns nor influence vulnerable lines through dependencies, reporting AUC 70-88% and F1 82-94% on 115K predictions.
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How Far Are We from Generating Missing Modalities with Foundation Models?
Evaluates 42 variants of foundation models across three formalized paradigms for missing modality reconstruction, identifies shortfalls in semantic extraction and validation, and introduces an agentic framework that reduces FID by at least 14% for images and MER by at least 10% for text.
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Human-AI Collaboration and the Transformation of Software Engineering Work
This interpretive synthesis maps three coexisting software engineering paradigms (Traditional, Generative AI-Enabled, Agentic AI-Enabled), proposes a five-category competency framework, and derives nine testable propositions about the shift toward human-AI collaboration.
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From Helpful to Trustworthy: LLM Agents for Pair Programming
A research proposal for three studies on multi-agent LLM pair programming that externalizes intent and uses automated validation to increase trustworthiness.