22 citations · 36 across the 17 of their papers we have counts for
8 papers · 1 filter
DSEffi-Bench: Demystifying Large Language Models' Capability in Efficient Data Science Code Generation
Zhihao Gong, Junzhe Yu, Dong Huang +3
Current data science (DS) code generation benchmarks equate correctness with quality, overlooking execution time differences that span orders of magnitude between correct solutions…
COMPAS: Difficulty-Aware Joint Search for Optimizing Code Generation
Jingzhi Gong, Jie M. Zhang, Gunel Jahangirova +3
Code generation systems make each LLM call with a model, a prompt, and decoding settings. However, existing optimization methods usually tune only part of these choices or use one…
Measuring the Influence of Incorrect Code on Test Generation
Dong Huang, Jie M. Zhang, Mark Harman +2
It is natural to suppose that a Large Language Model is more likely to generate correct test cases when prompted with correct code under test, compared to incorrect code under test…
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…
Themis: Automatic and Efficient Deep Learning System Testing with Strong Fault Detection Capability
Dong Huang, Tsz On Li, Xiaofei Xie +1
Deep Learning Systems (DLSs) have been widely applied in safety-critical tasks such as autopilot. However, when a perturbed input is fed into a DLS for inference, the DLS often has…
EffiBench: Benchmarking the Efficiency of Automatically Generated Code
Dong Huang, Yuhao Qing, Weiyi Shang +2
Code generation models have increasingly become integral to aiding software development. Although current research has thoroughly examined the correctness of the code produced by c…
CodeCoT: Tackling Code Syntax Errors in CoT Reasoning for Code Generation
Dong Huang, Qingwen Bu, Yuhao Qing +1
Chain-of-thought (CoT) has emerged as a groundbreaking tool in NLP, notably for its efficacy in complex reasoning tasks, such as mathematical proofs. However, its application in co…
Feature Map Testing for Deep Neural Networks
Dong Huang, Qingwen Bu, Yahao Qing +2
Due to the widespread application of deep neural networks~(DNNs) in safety-critical tasks, deep learning testing has drawn increasing attention. During the testing process, test ca…