9 citations · 11 across the 14 of their papers we have counts for
14 papers
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…
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…
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…
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…
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…
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…
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…
Comparing AI Coding Agents: A Task-Stratified Analysis of Pull Request Acceptance
Giovanni Pinna, Jingzhi Gong, David Williams +1
The rapid adoption of AI-powered coding assistants is transforming software development practices, yet systematic comparisons of their effectiveness across different task types and…
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…
Generative AI for Testing of Autonomous Driving Systems: A Survey
Qunying Song, He Ye, Mark Harman +1
Autonomous driving systems (ADS) have been an active area of research, with the potential to deliver significant benefits to society. However, before large-scale deployment on publ…
When Prompts Go Wrong: Evaluating Code Model Robustness to Ambiguous, Contradictory, and Incomplete Task Descriptions
Maya Larbi, Amal Akli, Mike Papadakis +4
Large Language Models (LLMs) have demonstrated impressive performance in code generation tasks under idealized conditions, where task descriptions are clear and precise. However, i…
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…
Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices
Luís Cruz, João Paulo Fernandes, Maja H. Kirkeby +22
The environmental impact of Artificial Intelligence (AI)-enabled systems is increasing rapidly, and software engineering plays a critical role in developing sustainable solutions.…
Automated Repair of Ambiguous Problem Descriptions for LLM-Based Code Generation
Haoxiang Jia, Robbie Morris, He Ye +2
The growing use of large language models (LLMs) has increased the importance of natural language (NL) in software engineering. However, ambiguity of NL can harm software quality, a…