45 citations · 88 across the 7 of their papers we have counts for
7 papers
On the Evaluation of Neural Code Translation: Taxonomy and Benchmark
Mingsheng Jiao, Tingrui Yu, Xuan Li +3
In recent years, neural code translation has gained increasing attention. While most of the research focuses on improving model architectures and training processes, we notice that…
InfeRE: Step-by-Step Regex Generation via Chain of Inference
Shuai Zhang, Xiaodong Gu, Yuting Chen +1
Automatically generating regular expressions (abbrev. regexes) from natural language description (NL2RE) has been an emerging research area. Prior studies treat regex as a linear s…
Diet Code Is Healthy: Simplifying Programs for Pre-trained Models of Code
Zhaowei Zhang, Hongyu Zhang, Beijun Shen +1
Pre-trained code representation models such as CodeBERT have demonstrated superior performance in a variety of software engineering tasks, yet they are often heavy in complexity, q…
Zero-Shot Program Representation Learning
Nan Cui, Yuze Jiang, Xiaodong Gu +1
Learning program representations has been the core prerequisite of code intelligent tasks such as code search and code clone detection. The state-of-the-art pre-trained models such…
Cross-Domain Deep Code Search with Meta Learning
Yitian Chai, Hongyu Zhang, Beijun Shen +1
Recently, pre-trained programming language models such as CodeBERT have demonstrated substantial gains in code search. Despite showing great performance, they rely on the availabil…
An Empirical Study on Tensor Shape Faults in Deep Learning Systems
Dangwei Wu, Beijun Shen, Yuting Chen
Software developers frequently adopt deep learning (DL) libraries to incorporate learning solutions into software systems. However, misuses of these libraries can cause various DL…
Locating Faulty Methods with a Mixed RNN and Attention Model
Shouliang Yang, Junming Cao, Hushuang Zeng +2
IR-based fault localization approaches achieves promising results when locating faulty files by comparing a bug report with source code. Unfortunately, they become less effective t…