From the 1 of 13 linked papers with an AI index.
13 papers
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-…
KQFuzz: Knowledge-Guided Fuzzing for Quantum Libraries via Large Language Models
Fuyuan Xia, Qixin Zhang, Chenhao Ying +5
The paper introduces KQFuzz, a knowledge-guided fuzzing framework that uses large language models to generate and mutate test programs for quantum libraries, achieving higher cover…
EvoClawBench: Can Agents Learn Reusable Skills from Their Own Runs?
Zhiyuan Peng, Xin Yin, Chenhao Ying +5
Existing agent benchmarks primarily test task completion, tool use, or skill utility, but do not isolate whether a runtime can convert evidence from its own runs into reusable skil…
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…
ArkEval: Benchmarking and Evaluating Automated CodeRepair for ArkTS
Bang Xie, Senjian Zhang, Zhiyuan Peng +3
Large language models have transformed code generation, enabling unprecedented automation in software development. As mobile ecosystems evolve, HarmonyOS has emerged as a critical…