54 citations · 111 across the 10 of their papers we have counts for
10 papers
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 generation for Information Technology Tasks in YAML through Large Language Models
Saurabh Pujar, Luca Buratti, Xiaojie Guo +8
The recent improvement in code generation capabilities due to the use of large language models has mainly benefited general purpose programming languages. Domain specific languages…
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…
Data-Driven AI Model Signal-Awareness Enhancement and Introspection
Sahil Suneja, Yufan Zhuang, Yunhui Zheng +2
AI modeling for source code understanding tasks has been making significant progress, and is being adopted in production development pipelines. However, reliability concerns, espec…
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…
Software Vulnerability Detection via Deep Learning over Disaggregated Code Graph Representation
Yufan Zhuang, Sahil Suneja, Veronika Thost +3
Identifying vulnerable code is a precautionary measure to counter software security breaches. Tedious expert effort has been spent to build static analyzers, yet insecure patterns…
D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis
Yunhui Zheng, Saurabh Pujar, Burn Lewis +6
Static analysis tools are widely used for vulnerability detection as they understand programs with complex behavior and millions of lines of code. Despite their popularity, static…
Probing Model Signal-Awareness via Prediction-Preserving Input Minimization
Sahil Suneja, Yunhui Zheng, Yufan Zhuang +2
This work explores the signal awareness of AI models for source code understanding. Using a software vulnerability detection use case, we evaluate the models' ability to capture th…
Exploring Software Naturalness through Neural Language Models
Luca Buratti, Saurabh Pujar, Mihaela Bornea +8
The Software Naturalness hypothesis argues that programming languages can be understood through the same techniques used in natural language processing. We explore this hypothesis…
Learning to map source code to software vulnerability using code-as-a-graph
Sahil Suneja, Yunhui Zheng, Yufan Zhuang +2
We explore the applicability of Graph Neural Networks in learning the nuances of source code from a security perspective. Specifically, whether signatures of vulnerabilities in sou…