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 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 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 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…

This paper presents a case study on using AI coding agents in software engineering and develops a process model for governing high-velocity agentic implementation.

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

The paper introduces POIROT, a novel protocol that uses the agents within a multi-agent system itself to diagnose and detect failures, demonstrating superior performance over traditional evaluation me…

The paper introduces SABER, a new benchmark that evaluates the operational safety of LLM coding agents in complex, stateful project environments, finding that current models have a high rate of harmfu…

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

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

The paper introduces HORIZON, a self-evolving agent framework for hardware design using git operations.

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

This tutorial explores advances and challenges in deploying large language model-based agentic systems across industries, with a focus on reasoning and planning, multi-agent coordination, and evaluati…

The paper argues that current 'on-the-fly' AI agent design lacks necessary software engineering rigor and proposes an 'AI Workflow Store' to provide hardened, reusable, and reliable agent workflows.

This paper presents AgentLocate, a framework for failure localization in large language model-based multi-agent systems using an LLM-based judging mechanism and multi-perspective verification.

ContraFix is an agentic framework that improves automated vulnerability repair by using differential runtime evidence to pinpoint the root cause of bugs, achieving state-of-the-art performance on majo…