114 citations · 165 across the 16 of their papers we have counts for
17 papers
SWE-Spot: Building Small Repo-Experts with Repository-Centric Learning
Jinjun Peng, Magnus Saebo, Tianjun Zhong +6
The deployment of coding agents in privacy-sensitive and resource-constrained environments drives the demand for capable open-weight Small Language Models (SLMs). However, they suf…
SemCoder: Training Code Language Models with Comprehensive Semantics Reasoning
Yangruibo Ding, Jinjun Peng, Marcus J. Min +3
Code Large Language Models (Code LLMs) have excelled at tasks like code completion but often miss deeper semantics such as execution effects and dynamic states. This paper aims to…
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…
CrossCodeEval: A Diverse and Multilingual Benchmark for Cross-File Code Completion
Yangruibo Ding, Zijian Wang, Wasi Uddin Ahmad +8
Code completion models have made significant progress in recent years, yet current popular evaluation datasets, such as HumanEval and MBPP, predominantly focus on code completion t…
Beyond Accuracy: Evaluating Self-Consistency of Code Large Language Models with IdentityChain
Marcus J. Min, Yangruibo Ding, Luca Buratti +4
Code Large Language Models (Code LLMs) are being increasingly employed in real-life applications, so evaluating them is critical. While the conventional accuracy evaluates the perf…
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…
CoCoMIC: Code Completion By Jointly Modeling In-file and Cross-file Context
Yangruibo Ding, Zijian Wang, Wasi Uddin Ahmad +5
While pre-trained language models (LM) for code have achieved great success in code completion, they generate code conditioned only on the contents within the file, i.e., in-file c…
NatGen: Generative pre-training by "Naturalizing" source code
Saikat Chakraborty, Toufique Ahmed, Yangruibo Ding +2
Pre-trained Generative Language models (e.g. PLBART, CodeT5, SPT-Code) for source code yielded strong results on several tasks in the past few years, including code generation and…
VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatements
Yangruibo Ding, Sahil Suneja, Yunhui Zheng +4
Automatically locating vulnerable statements in source code is crucial to assure software security and alleviate developers' debugging efforts. This becomes even more important in…
Towards Learning (Dis)-Similarity of Source Code from Program Contrasts
Yangruibo Ding, Luca Buratti, Saurabh Pujar +3
Understanding the functional (dis)-similarity of source code is significant for code modeling tasks such as software vulnerability and code clone detection. We present DISCO(DIS-si…
Deep Learning based Vulnerability Detection: Are We There Yet?
Saikat Chakraborty, Rahul Krishna, Yangruibo Ding +1
Automated detection of software vulnerabilities is a fundamental problem in software security. Existing program analysis techniques either suffer from high false positives or false…
Patching as Translation: the Data and the Metaphor
Yangruibo Ding, Baishakhi Ray, Premkumar Devanbu +1
Machine Learning models from other fields, like Computational Linguistics, have been transplanted to Software Engineering tasks, often quite successfully. Yet a transplanted model'…
CODIT: Code Editing with Tree-Based Neural Models
Saikat Chakraborty, Yangruibo Ding, Miltiadis Allamanis +1
The way developers edit day-to-day code tends to be repetitive, often using existing code elements. Many researchers have tried to automate repetitive code changes by learning from…