most citedGreening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices

9 citations · 11 across the 14 of their papers we have counts for

collaborators

14 papers

cs.SE2026

How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Study

Yunbo Lyu, David Williams, Jieke Shi +5

The rise of Software Engineering (SE) agents, i.e., LLM-based agents that can understand large codebases and carry out engineering tasks with limited human intervention, has been m…

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.AI2026

TerminalWorld: Benchmarking Agents on Real-World Terminal Tasks

Zhaoyang Chu, Jiarui Hu, Xingyu Jiang +8

We introduce TerminalWorld, a scalable data engine that automatically reverse-engineers high-fidelity evaluation tasks from "in-the-wild" terminal recordings. Processing 80,870 ter…

cs.SE2026

Echo: Graph-Enhanced Retrieval and Execution Feedback for Issue Reproduction Test Generation

Zhiwei Fei, Yue Pan, Federica Sarro +4

Identifying the root cause of a bug remains difficult for many developers because bug reports often lack a bug reproducing test case that reliably triggers the failure. Manually wr…

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.LG2026

ContextBench: A Benchmark for Context Retrieval in Coding Agents

Han Li, Letian Zhu, Bohan Zhang +7

LLM-based coding agents have shown strong performance on automated issue resolution benchmarks, yet existing evaluations largely focus on final task success, providing limited insi…