1 citations · 3 across the 14 of their papers we have counts for
9 papers · 1 filter
PonyEval: Evaluating LLM-Based Program Repair for Capability-Safe and Actor-Oriented Pony Software
Bang Xie, Hao Liu, Zhenyu Shi +8
Repository-level issue-resolution benchmarks have made executable evaluation central to software-engineering agents, but their language coverage remains concentrated in mainstream…
OdinEval: A Reproducible Benchmark for LLM-Based Program Repair in the Odin Programming Language
Bang Xie, Hao Liu, Zhiyuan Peng +8
Repository-level repair benchmarks still center on a few mainstream languages, leaving systems languages such as Odin largely untested. We present OdinEval, a reproducible benchmar…
AppEval: A Unified Benchmark for LLM-Based Mobile Application Repair in ArkTS, Swift, and Kotlin
Bang Xie, Hao Liu, Zhenyu Shi +11
Repository-level LLM agents are typically evaluated on projects whose tests run on the build host. It remains unclear whether their repairs survive the mobile build-install-launch-…
PlayCoder: Making LLM-Generated GUI Code Playable
Zhiyuan Peng, Wei Tao, Xin Yin +3
Large language models (LLMs) have achieved strong results in code generation, but their ability to generate GUI applications, especially games, remains insufficiently studied. Exis…
SolAgent: A Specialized Multi-Agent Framework for Solidity Code Generation
Wei Chen, Zhiyuan Peng, Xin Yin +4
Smart contracts are the backbone of the decentralized web, yet ensuring their functional correctness and security remains a critical challenge. While Large Language Models (LLMs) h…
RepoGenesis: Benchmarking End-to-End Microservice Generation from Readme to Repository
Zhiyuan Peng, Xin Yin, Pu Zhao +7
Large language models and agents have achieved remarkable progress in code generation. However, existing benchmarks focus on isolated function/class-level generation (e.g., ClassEv…