7 papers
Multi-level Code Optimization via Mixture of Prompts
Yun Peng, Jun Wan, Jiakun Liu +3
Runtime efficiency is a critical factor that impacts both software quality and user satisfaction. There are many approaches proposed for code optimization to improve runtime effici…
Identifying Good and Bad Neurons for Task-Level Controllable LLMs
Wenjie Li, Guansong Pang, Hezhe Qiao +2
Large Language Models have demonstrated remarkable capabilities on multiple-choice question answering benchmarks, but the complex mechanisms underlying their large-scale neurons re…
Curiosity-Driven Testing for Sequential Decision-Making Process
Junda He, Zhou Yang, Jieke Shi +5
Sequential decision-making processes (SDPs) are fundamental for complex real-world challenges, such as autonomous driving, robotic control, and traffic management. While recent adv…
Characterising Bugs in Jupyter Platform
Yutian Tang, Hongchen Cao, Yuxi Chen +1
As a representative literate programming platform, Jupyter is widely adopted by developers, data analysts, and researchers for replication, data sharing, documentation, interactive…
When Deep Learning Meets Information Retrieval-based Bug Localization: A Survey
Feifei Niu, Chuanyi Li, Kui Liu +2
Bug localization is a crucial aspect of software maintenance, running through the entire software lifecycle. Information retrieval-based bug localization (IRBL) identifies buggy co…
FlexFL: Flexible and Effective Fault Localization with Open-Source Large Language Models
Chuyang Xu, Zhongxin Liu, Xiaoxue Ren +3
Due to the impressive code comprehension ability of Large Language Models (LLMs), a few studies have proposed to leverage LLMs to locate bugs, i.e., LLM-based FL, and demonstrated…