11 citations · 16 across the 8 of their papers we have counts for
9 papers · 1 filter
CYCLE: Learning to Self-Refine the Code Generation
Yangruibo Ding, Marcus J. Min, Gail Kaiser +1
Pre-trained code language models have achieved promising performance in code generation and improved the programming efficiency of human developers. However, their self-refinement…
Vulnerability Detection with Code Language Models: How Far Are We?
Yangruibo Ding, Yanjun Fu, Omniyyah Ibrahim +6
In the context of the rising interest in code language models (code LMs) and vulnerability detection, we study the effectiveness of code LMs for detecting vulnerabilities. Our anal…
TRACED: Execution-aware Pre-training for Source Code
Yangruibo Ding, Ben Steenhoek, Kexin Pei +3
Most existing pre-trained language models for source code focus on learning the static code text, typically augmented with static code structures (abstract syntax tree, dependency…
CONCORD: Clone-aware Contrastive Learning for Source Code
Yangruibo Ding, Saikat Chakraborty, Luca Buratti +4
Deep Learning (DL) models to analyze source code have shown immense promise during the past few years. More recently, self-supervised pre-training has gained traction for learning…
Automated Code Editing with Search-Generate-Modify
Changshu Liu, Pelin Cetin, Yogesh Patodia +3
Code editing is essential in evolving software development. Many automated code editing tools have been proposed that leverage both Information Retrieval-based techniques and Machi…
TraceFixer: Execution Trace-Driven Program Repair
Islem Bouzenia, Yangruibo Ding, Kexin Pei +2
When debugging unintended program behavior, developers can often identify the point in the execution where the actual behavior diverges from the desired behavior. For example, a va…