ASE 2025
Sun 16 - Thu 20 November 2025 Seoul, South Korea

This program is tentative and subject to change.

Mon 17 Nov 2025 11:25 - 11:38 at Grand Hall 4 - Efficiency & Fairness 1

Large Language Models (LLMs) have demonstrated significant capability in code generation, but their potential in code optimization remains underexplored. Previous LLM-based code optimization approaches exclusively focus on function-level optimization and overlook interaction between functions, failing to generalize to real-world development scenarios. Code editing techniques show great potential for conducting project-level code optimization, yet they face challenges associated with invalid edits and suboptimal internal functions. To address these gaps, we propose PEACE, a novel hybrid framework for \textbf{P}roject-level p\textbf{E}rformance optimization through \textbf{A}utomatic \textbf{C}ode \textbf{E}diting, which also ensures the overall correctness and integrity of the project. PEACE integrates three key phases: dependency-aware optimizing function sequence construction, valid associated edits identification, and performance editing iteration. To rigorously evaluate the effectiveness of PEACE, we construct PEACExec, the first benchmark comprising 146 real-world optimization tasks from 47 high-impact GitHub Python projects, along with highly qualified test cases and executable environments. Extensive experiments demonstrate PEACE’s superiority over the state-of-the-art baselines, achieving a 69.2% correctness rate (pass@1) and +46.9% opt rate in execution efficiency. Notably, our PEACE outperforms all baselines by significant margins, particularly in complex optimization tasks with multiple functions. Moreover, extensive experiments are also conducted to validate the contributions of each component in PEACE, as well as the rationale and effectiveness of our hybrid framework design.

This program is tentative and subject to change.

Mon 17 Nov

Displayed time zone: Seoul change

11:00 - 12:30
Efficiency & Fairness 1Research Papers at Grand Hall 4
11:00
12m
Talk
AutoFid: Adaptive and Noise-Aware Fidelity Measurement for Quantum Programs via Circuit Graph Analysis
Research Papers
Tingting Li Zhejiang University, Ziming Zhao Zhejiang University, Jianwei Yin Zhejiang University
11:12
12m
Talk
HybridSIMD: A Super C++ SIMD Library with Integrated Auto-tuning Capabilities
Research Papers
Haolin Pan Institute of Software, Chinese Academy of Sciences;School of Intelligent Science and Technology, HIAS, UCAS, Hangzhou;University of Chinese Academy of Sciences, Xulin Zhou Institute of Software, Chinese Academy of Sciences; University of Chinese Academy of Sciences, Mingjie Xing Institute of Software, Chinese Academy of Sciences, Yanjun Wu Institute of Software, Chinese Academy of Sciences
11:25
12m
Talk
PEACE: Towards Efficient Project-Level Performance Optimization via Hybrid Code Editing
Research Papers
Xiaoxue Ren Zhejiang University, Jun Wan Zhejiang University, Yun Peng The Chinese University of Hong Kong, Zhongxin Liu Zhejiang University, Ming Liang Ant Group, Dajun Chen Ant Group, Wei Jiang Ant Group, Yong Li Ant Group
11:38
12m
Talk
CoTune: Co-evolutionary Configuration Tuning
Research Papers
Gangda Xiong University of Electronic Science and Technology of China, Tao Chen University of Birmingham
Pre-print
11:51
12m
Talk
It's Not Easy Being Green: On the Energy Efficiency of Programming Languages
Research Papers
Nicolas van Kempen University of Massachusetts Amherst, USA, Hyuk-Je Kwon University of Massachusetts Amherst, Dung Nguyen University of Massachusetts Amherst, Emery D. Berger University of Massachusetts Amherst and Amazon Web Services
12:04
12m
Talk
When Faster Isn't Greener: The Hidden Costs of LLM-Based Code Optimization
Research Papers
Tristan Coignion Université de Lille - Inria, Clément Quinton Université de Lille, Romain Rouvoy University Lille 1 and INRIA
12:17
12m
Talk
United We Stand: Towards End-to-End Log-based Fault Diagnosis via Interactive Multi-Task Learning
Research Papers
Minghua He Peking University, Chiming Duan Peking University, Pei Xiao Peking University, Tong Jia Institute for Artificial Intelligence, Peking University, Beijing, China, Siyu Yu The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), Lingzhe Zhang Peking University, China, Weijie Hong Peking university, Jing Han ZTE Corporation, Yifan Wu Peking University, Ying Li School of Software and Microelectronics, Peking University, Beijing, China, Gang Huang Peking University