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cs.SE2026

When Code Becomes an LLM Call: An Empirical Study of Call-Site Contracts in Agentic Systems

Shihao Weng, Longtian Wang, Yang Feng +6

Software quality has long rested on specifications that state what code must do, and on testing and verification that check that the code does it. Agentic systems change this found…

cs.SE2026

LoopsBench: From Harness Engineering to Loop Engineering in Coding Agent Evaluation

Han Li, Zhemin Fang, Rili Feng +8

Coding agent infrastructure is shifting from harness engineering toward loop engineering as coding agents are deployed for sustained long-horizon software development. Existing ben…

cs.SE2026

Failure as a Process: An Anatomy of CLI Coding Agent Trajectories

Xiangxin Zhao, Han Li, Shuaiting Li +4

Large language model (LLM) coding agents are increasingly deployed to autonomously perform software engineering tasks in terminal-based environments, making their reliability a gro…

cs.SE2026

An Iterative Test-and-Repair Framework for Competitive Code Generation

Lingxiao Tang, Muyang Ye, Zhaoyang Chu +4

Large language models (LLMs) have made remarkable progress in code generation, but competitive programming remains a challenge. Recent training-based methods have improved code gen…

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…