This paper revisits N-version programming with AI coding agents and finds substantial common-mode failures but also practical benefits.

This paper introduces TRIM, an algorithm that minimizes redundant edits in AI-generated code, called CodeSlop, by minimizing agent trajectories, reducing CodeSlop by 17.9%-32.9% with negligible perfor…

This study analyzes over 20,000 real-world coding sessions to show that AI coding agents frequently fail users through subtle misalignment, requiring constant manual correction even when major system…

The paper introduces a data-centric optimization pipeline to improve coding agents' ability to interact with a branching lakehouse, showing significant accuracy gains by treating agent evaluation as a…

This paper characterizes oracle signals in test files of agent-authored pull requests and assesses their impact on merge outcomes.

The paper introduces PithTrain, a compact, agent-native Mixture-of-Experts (MoE) training framework that significantly improves agent-task efficiency compared to existing production stacks.

This paper introduces CORE-Bench, a comprehensive benchmark for code retrieval in agentic coding.

DeepSWE is a new benchmark for evaluating coding agents with original tasks written from scratch and hand-graded verifiers.

This paper studies the problem of retrospectively reconstructing commit history from squashed patches, and evaluates different methods using a benchmark dataset.

The paper reports on penetration tests conducted on proprietary, large-scale AI agent systems, finding that security vulnerabilities persist despite stricter development standards.

This paper analyzes 25,264 agentic pull requests from 2,361 GitHub repositories to investigate adoption, productivity, and collaboration patterns of agentic coding tools.

The paper analyzes how agentic AI coding assistants can be compromised via prompt injection attacks embedded in external artifacts, turning them into unauthorized execution shells for attackers.

The paper introduces Language-Based Agent Control (LBAC), a new programming model that extends static typing and runtime enforcement guarantees to agentic applications, ensuring that agent-generated c…

This paper measures the integration friction in code repositories caused by autonomous coding agents and finds that about half of the variation in friction is repository-level, and agent-authored cont…

The study found that while multi-agent LLM code generation architectures significantly affect code complexity, the added complexity does not translate into better functional correctness, suggesting ar…

The paper introduces LACUNA, a novel programming model that allows LLM agents to write code that shapes the runtime environment while maintaining strong type-checking safety guarantees.

This paper presents an empirical study on how developers respond to agentic code reviews using CodeRabbit, revealing mixed reception and opportunities for improvement.