3 citations · 7 across the 7 of their papers we have counts for
7 papers
COBOL-Coder: Domain-Adapted Large Language Models for COBOL Code Generation and Translation
Anh T. V. Dau, Shin Hwei Tan, Jinqiu Yang +2
COBOL remains a critical language for mainframe systems, yet existing large language models (LLMs) struggle to generate and translate COBOL code correctly. This paper reports our e…
COBOLAssist: Analyzing and Fixing Compilation Errors for LLM-Powered COBOL Code Generation
Anh T. V. Dau, Shin Hwei Tan, Jinqiu Yang +2
Legacy programming languages such as COBOL (Common Business-Oriented Language) remain critical in business computing. However, maintaining legacy COBOL systems is increasingly chal…
XMainframe: A Large Language Model for Mainframe Modernization
Anh T. V. Dau, Hieu Trung Dao, Anh Tuan Nguyen +3
Mainframe operating systems, despite their inception in the 1940s, continue to support critical sectors like finance and government. However, these systems are often viewed as outd…
DocChecker: Bootstrapping Code Large Language Model for Detecting and Resolving Code-Comment Inconsistencies
Anh T. V. Dau, Jin L. C. Guo, Nghi D. Q. Bui
Comments within source code are essential for developers to comprehend the code's purpose and ensure its correct usage. However, as codebases evolve, maintaining an accurate alignm…
Class based Influence Functions for Error Detection
Thang Nguyen-Duc, Hoang Thanh-Tung, Quan Hung Tran +4
Influence functions (IFs) are a powerful tool for detecting anomalous examples in large scale datasets. However, they are unstable when applied to deep networks. In this paper, we…
The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation
Dung Nguyen Manh, Nam Le Hai, Anh T. V. Dau +4
We present The Vault, a dataset of high-quality code-text pairs in multiple programming languages for training large language models to understand and generate code. We present met…
Towards Using Data-Influence Methods to Detect Noisy Samples in Source Code Corpora
Anh T. V. Dau, Thang Nguyen-Duc, Hoang Thanh-Tung +1
Despite the recent trend of developing and applying neural source code models to software engineering tasks, the quality of such models is insufficient for real-world use. This is…