416 citations · 792 across the 31 of their papers we have counts for
9 papers · 1 filter
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…
Reinforcement Learning from Automatic Feedback for High-Quality Unit Test Generation
Benjamin Steenhoek, Michele Tufano, Neel Sundaresan +1
Software testing is a crucial aspect of software development, and the creation of high-quality tests that adhere to best practices is essential for effective maintenance. Recently,…
Predicting Code Coverage without Execution
Michele Tufano, Shubham Chandel, Anisha Agarwal +2
Code coverage is a widely used metric for quantifying the extent to which program elements, such as statements or branches, are executed during testing. Calculating code coverage i…
RAPGen: An Approach for Fixing Code Inefficiencies in Zero-Shot
Spandan Garg, Roshanak Zilouchian Moghaddam, Neel Sundaresan
Performance bugs are non-functional bugs that can even manifest in well-tested commercial products. Fixing these performance bugs is an important yet challenging problem. In this w…