most citedUnderstanding Code Agent Behaviour: An Empirical Study of Success and Failure Trajectories

1 citations · 1 across the 10 of their papers we have counts for

collaborators

11 papers

cs.SE2026

Zero2Repo: Can Coding Agents Build Repositories from Scratch?

Pei Yang, Tianyu Shi, Yuhang Yao +23

Coding agents are increasingly asked to build software rather than patch it, yet benchmarks for from-scratch repository construction are mostly limited to a single language and dep…

cs.AI2026

Harbor Adapters and Harbor-Index: Infrastructure and a Curated Meta-Dataset for Large-Scale Agentic Evaluation

Lin Shi, Haowei Lin, Zixuan Zhu +123

Evaluating agents on the growing number of agentic benchmarks is challenging because they often require complex environments and agent integrations. We introduce Harbor Adapters, a…

cs.AI2026

SWE-chat: Coding Agent Interactions From Real Users in the Wild

Joachim Baumann, Vishakh Padmakumar, Xiang Li +3

AI coding agents are being adopted at scale, yet we lack empirical evidence on how people actually use them and how much of their output is useful in practice. We present SWE-chat,…

cs.CR2026

Trojan's Whisper: Stealthy Manipulation of OpenClaw through Injected Bootstrapped Guidance

Fazhong Liu, Zhuoyan Chen, Tu Lan +6

Autonomous coding agents are increasingly integrated into software development workflows, offering capabilities that extend beyond code suggestion to active system interaction and…

cs.SE2026

SWE-QA-Pro: A Representative Benchmark and Scalable Training Recipe for Repository-Level Code Understanding

Songcheng Cai, Zhiheng Lyu, Yuansheng Ni +13

Agentic repository-level code understanding is essential for automating complex software engineering tasks, yet the field lacks reliable benchmarks. Existing evaluations often over…

cs.LG2026

AOI: Turning Failed Trajectories into Training Signals for Autonomous Cloud Diagnosis

Pei Yang, Wanyi Chen, Asuka Yuxi Zheng +13

Large language model (LLM) agents offer a promising data-driven approach to automating Site Reliability Engineering (SRE), yet their enterprise deployment is constrained by three c…

cs.SE2026

HerAgent: Rethinking the Automated Environment Deployment via Hierarchical Test Pyramid

Xiang Li, Siyu Lu, Federica Sarro +2

Automated software environment setup is a prerequisite for testing, debugging, and reproducing failures, yet remains challenging in practice due to complex dependencies, heterogene…

cs.SE2026

Environment-in-the-Loop: Rethinking Code Migration with LLM-based Agents

Xiang Li, Zhiwei Fei, Ying Ma +3

Modern software systems continuously undergo code upgrades to enhance functionality, security, and performance, and Large Language Models (LLMs) have demonstrated remarkable capabi…

cs.SE2026

JMigBench: A Benchmark for Evaluating LLMs on Source Code Migration (Java 8 to Java 11)

Nishil Amin, Zhiwei Fei, Xiang Li +2

We build a benchmark to evaluate large language models (LLMs) for source code migration tasks, specifically upgrading functions from Java 8 to Java 11. We first collected a dataset…

cs.SE2025★ 1 cited

Understanding Code Agent Behaviour: An Empirical Study of Success and Failure Trajectories

Oorja Majgaonkar, Zhiwei Fei, Xiang Li +2

The increasing deployment of Large Language Model (LLM) agents for complex software engineering tasks has created a need to understand their problem-solving behaviours beyond simpl…

cs.SE2025

Prometheus: Towards Long-Horizon Codebase Navigation for Repository-Level Problem Solving

Yue Pan, Zimin Chen, Siyu Lu +8

Large Language Models (LLMs) have shown remarkable capabilities in automating software engineering tasks, spurring the emergence of coding agents that scaffold LLMs with external t…