81 citations · 301 across the 80 of their papers we have counts for
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cs.SE2026
CodeSpecBench: Benchmarking LLMs for Executable Behavioral Specification Generation
Zaoyu Chen, Jianbo Dai, Boyu Zhu +6
Large language models (LLMs) can generate code from natural language, but the extent to which they capture intended program behavior remains unclear. Executable behavioral specific…
cs.SE2025★ 1 cited
A Comprehensive Survey on Benchmarks and Solutions in Software Engineering of LLM-Empowered Agentic System
Jiale Guo, Suizhi Huang, Mei Li +8
The integration of Large Language Models (LLMs) into software engineering has driven a transition from traditional rule-based systems to autonomous agentic systems capable of solvi…
cs.SE2024
EffiLearner: Enhancing Efficiency of Generated Code via Self-Optimization
Dong Huang, Jianbo Dai, Han Weng +5
Large language models (LLMs) have shown remarkable progress in code generation, but their generated code often suffers from inefficiency, resulting in longer execution times and hi…