98 citations · 287 across the 45 of their papers we have counts for
27 papers · 1 filter
Beyond Repository Boundaries: Cross-Repository Graph Retrieval for Code Generation
Minh Le-Anh, Nam Le Hai, Quyen Tran +4
Repository-level code generation requires generated code to be compatible not only with the target repository but also with its dependency environment. Existing retrieval-based met…
Documentation-Guided Agentic Codebase Migration from C to Rust
Minh Le-Anh, Anh Nguyen Hoang, Bach Le +1
Migrating legacy C repositories to Rust promises stronger memory safety, but existing translators often work at the level of files or functions and miss architectural intent. We pr…
Agentic Coding Needs Proactivity, Not Just Autonomy
Nghi D. Q. Bui, Georgios Evangelopoulos
Coding agents are rapidly changing the landscape of software development, moving from inline completion to autonomous systems that edit repositories, open pull requests, respond to…
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…
Do Not Treat Code as Natural Language: Implications for Repository-Level Code Generation and Beyond
Minh Le-Anh, Huyen Nguyen, Khanh An Tran +4
Large language models for code (CodeLLMs) have demonstrated remarkable success in standalone code completion and generation, sometimes even surpassing human performance, yet their…
SWE-EVO: Benchmarking Coding Agents in Long-Horizon Software Evolution Scenarios
Tue Le, Minh V. T. Thai, Dung Nguyen Manh +2
Existing benchmarks for AI coding agents focus on isolated, single-issue tasks such as fixing a bug or adding a small feature. However, real-world software engineering is a long-ho…
CodeWiki: Evaluating AI's Ability to Generate Holistic Documentation for Large-Scale Codebases
Anh Nguyen Hoang, Minh Le-Anh, Bach Le +1
Given a large and evolving codebase, the ability to automatically generate holistic, architecture-aware documentation that captures not only individual functions but also cross-fil…
SWE-Synth: Synthesizing Verifiable Bug-Fix Data to Enable Large Language Models in Resolving Real-World Bugs
Minh V. T. Pham, Huy N. Phan, Hoang N. Phan +3
Large language models (LLMs) are transforming automated program repair (APR) through agent-based approaches that localize bugs, generate patches, and verify fixes. However, the lac…
VisualCoder: Guiding Large Language Models in Code Execution with Fine-grained Multimodal Chain-of-Thought Reasoning
Cuong Chi Le, Hoang-Chau Truong-Vinh, Huy Nhat Phan +3
Predicting program behavior and reasoning about code execution remain significant challenges in software engineering, particularly for large language models (LLMs) designed for cod…
CodeMMLU: A Multi-Task Benchmark for Assessing Code Understanding & Reasoning Capabilities of CodeLLMs
Dung Nguyen Manh, Thang Phan Chau, Nam Le Hai +4
Recent advances in Code Large Language Models (CodeLLMs) have primarily focused on open-ended code generation, often overlooking the crucial aspect of code understanding and reason…
HyperAgent: Generalist Software Engineering Agents to Solve Coding Tasks at Scale
Huy Nhat Phan, Tien N. Nguyen, Phong X. Nguyen +1
Large Language Models (LLMs) have revolutionized software engineering (SE), showcasing remarkable proficiency in various coding tasks. Despite recent advancements that have enabled…
CodeFlow: Program Behavior Prediction with Dynamic Dependencies Learning
Cuong Chi Le, Hoang Nhat Phan, Huy Nhat Phan +2
Predicting program behavior without execution is a critical task in software engineering. Existing models often fall short in capturing the dynamic dependencies among program eleme…
AgileCoder: Dynamic Collaborative Agents for Software Development based on Agile Methodology
Minh Huynh Nguyen, Thang Phan Chau, Phong X. Nguyen +1
Software agents have emerged as promising tools for addressing complex software engineering tasks. Existing works, on the other hand, frequently oversimplify software development w…
On the Impacts of Contexts on Repository-Level Code Generation
Nam Le Hai, Dung Manh Nguyen, Nghi D. Q. Bui
CodeLLMs have gained widespread adoption for code generation tasks, yet their capacity to handle repository-level code generation with complex contextual dependencies remains under…
Envisioning the Next-Generation AI Coding Assistants: Insights & Proposals
Khanh Nghiem, Anh Minh Nguyen, Nghi D. Q. Bui
As a research-product hybrid group in AI for Software Engineering (AI4SE), we present four key takeaways from our experience developing in-IDE AI coding assistants. AI coding assis…
RepoHyper: Search-Expand-Refine on Semantic Graphs for Repository-Level Code Completion
Huy N. Phan, Hoang N. Phan, Tien N. Nguyen +1
Code Large Language Models (CodeLLMs) have demonstrated impressive proficiency in code completion tasks. However, they often fall short of fully understanding the extensive context…
Functional Overlap Reranking for Neural Code Generation
Hung Quoc To, Minh Huynh Nguyen, Nghi D. Q. Bui
Code Large Language Models (CodeLLMs) have ushered in a new era in code generation advancements. However, selecting the best code solutions from all possible CodeLLM outputs remain…
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…
CodeTF: One-stop Transformer Library for State-of-the-art Code LLMs
Nghi D. Q. Bui, Hung Le, Yue Wang +3
Code intelligence plays a key role in transforming modern software engineering. Recently, deep learning-based models, especially Transformer-based large language models (LLMs), hav…
Detect-Localize-Repair: A Unified Framework for Learning to Debug with CodeT5
Nghi D. Q. Bui, Yue Wang, Steven Hoi
Automated software debugging is a crucial task for improving the productivity of software developers. Many neural-based techniques have been proven effective for debugging-related…
HierarchyNet: Learning to Summarize Source Code with Heterogeneous Representations
Minh Huynh Nguyen, Nghi D. Q. Bui, Truong Son Hy +2
We propose a novel method for code summarization utilizing Heterogeneous Code Representations (HCRs) and our specially designed HierarchyNet. HCRs effectively capture essential cod…
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…
InferCode: Self-Supervised Learning of Code Representations by Predicting Subtrees
Nghi D. Q. Bui, Yijun Yu, Lingxiao Jiang
Building deep learning models on source code has found many successful software engineering applications, such as code search, code comment generation, bug detection, code migratio…