12 citations · 36 across the 12 of their papers we have counts for
12 papers
Is Next Token Prediction Sufficient for GPT? Exploration on Code Logic Comprehension
Mengnan Qi, Yufan Huang, Yongqiang Yao +3
Large language models (LLMs) has experienced exponential growth, they demonstrate remarkable performance across various tasks. Notwithstanding, contemporary research primarily cent…
AutoDev: Automated AI-Driven Development
Michele Tufano, Anisha Agarwal, Jinu Jang +2
The landscape of software development has witnessed a paradigm shift with the advent of AI-powered assistants, exemplified by GitHub Copilot. However, existing solutions are not le…
Copilot Evaluation Harness: Evaluating LLM-Guided Software Programming
Anisha Agarwal, Aaron Chan, Shubham Chandel +6
The integration of Large Language Models (LLMs) into Development Environments (IDEs) has become a focal point in modern software development. LLMs such as OpenAI GPT-3.5/4 and Code…
Rethinking the Instruction Quality: LIFT is What You Need
Yang Xu, Yongqiang Yao, Yufan Huang +4
Instruction tuning, a specialized technique to enhance large language model (LLM) performance via instruction datasets, relies heavily on the quality of employed data. Existing qua…
SUT: Active Defects Probing for Transcompiler Models
Mengnan Qi, Yufan Huang, Maoquan Wang +5
Automatic Program translation has enormous application value and hence has been attracting significant interest from AI researchers. However, we observe that current program transl…
Program Translation via Code Distillation
Yufan Huang, Mengnan Qi, Yongqiang Yao +4
Software version migration and program translation are an important and costly part of the lifecycle of large codebases. Traditional machine translation relies on parallel corpora…