activity
20232026
most citedAgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation

22 citations · 30 across the 19 of their papers we have counts for

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Showing 2024Show all

9 papers · 1 filter

cs.CL2024★ 1 cited

EffiCoder: Enhancing Code Generation in Large Language Models through Efficiency-Aware Fine-tuning

Dong Huang, Guangtao Zeng, Jianbo Dai +6

As large language models (LLMs) play an increasingly important role in code generation, enhancing both correctness and efficiency has become crucial. Current methods primarily focu…

cs.SE2024★ 2 cited

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…

cs.RO2024

OMEGA: Efficient Occlusion-Aware Navigation for Air-Ground Robot in Dynamic Environments via State Space Model

Junming Wang, Xiuxian Guan, Zekai Sun +4

Air-ground robots (AGRs) are widely used in surveillance and disaster response due to their exceptional mobility and versatility (i.e., flying and driving). Current AGR navigation…

cs.RO2024

Learning Manipulation by Predicting Interaction

Jia Zeng, Qingwen Bu, Bangjun Wang +12

Representation learning approaches for robotic manipulation have boomed in recent years. Due to the scarcity of in-domain robot data, prevailing methodologies tend to leverage larg…

cs.RO2024

Hybrid-Parallel: Achieving High Performance and Energy Efficient Distributed Inference on Robots

Zekai Sun, Xiuxian Guan, Junming Wang +6

The rapid advancements in machine learning techniques have led to significant achievements in various real-world robotic tasks. These tasks heavily rely on fast and energy-efficien…

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…

cs.SE2024

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…

cs.RO2024

AGRNav: Efficient and Energy-Saving Autonomous Navigation for Air-Ground Robots in Occlusion-Prone Environments

Junming Wang, Zekai Sun, Xiuxian Guan +6

The exceptional mobility and long endurance of air-ground robots are raising interest in their usage to navigate complex environments (e.g., forests and large buildings). However,…

cs.SE2024★ 4 cited

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…