9 citations · 28 across the 22 of their papers we have counts for
23 papers
A Data-Centric Framework for Detecting and Correcting Corrupted Labels
Ha-Linh Nguyen, Hong-Anh Nguyen, Minh-Duc La +3
The performance of machine learning and deep learning models largely depends on the quality of the training data. However, the quality of the real-world datasets is often compromis…
Noise-Aware Framework for Correcting Corrupted Labels
Ha-Linh Nguyen, Hong-Anh Nguyen, Minh-Duc La +4
High-quality labeled data is essential for training reliable ML/DL models. However, real-world datasets often contain a considerable proportion of corrupted labels, which can sever…
Structured Exploration and Exploitation of Label Functions for Automated Data Annotation
Phong Lam, Ha-Linh Nguyen, Thu-Trang Nguyen +2
High-quality labeled data is critical for training reliable machine learning and deep learning models, yet manual annotation remains costly and error-prone. Programmatic labeling a…
iML: Executable, Problem-Grounded, and Broadly Exploratory Code-Driven AutoML
Dat Le, Duc-Cuong Le, Anh-Son Nguyen +4
Automated Machine Learning (AutoML) has improved access to machine learning, yet existing techniques often remain limited in flexibility, transparency, and execution reliability. C…
Model-Agnostic Correctness Assessment for LLM-Generated Code via Dynamic Internal Representation Selection
Thanh Trong Vu, Tuan-Dung Bui, Thu-Trang Nguyen +2
Large Language Models (LLMs) have demonstrated impressive capabilities in code generation and are increasingly integrated into the software development process. However, ensuring t…
CABENCH: Benchmarking Composable AI for Solving Complex Tasks through Composing Ready-to-Use Models
Tung-Thuy Pham, Duy-Quan Luong, Minh-Quan Duong +4
Composable AI offers a scalable and effective paradigm for tackling complex AI tasks by decomposing them into sub-tasks and solving each sub-task using ready-to-use well-trained mo…
Correctness Assessment of Code Generated by Large Language Models Using Internal Representations
Tuan-Dung Bui, Thanh Trong Vu, Thu-Trang Nguyen +2
Ensuring the correctness of code generated by Large Language Models (LLMs) presents a significant challenge in AI-driven software development. Existing approaches predominantly rel…
Generating Critical Scenarios for Testing Automated Driving Systems
Trung-Hieu Nguyen, Truong-Giang Vuong, Hong-Nam Duong +4
Autonomous vehicles (AVs) have demonstrated significant potential in revolutionizing transportation, yet ensuring their safety and reliability remains a critical challenge, especia…
Impact of Code Transformation on Detection of Smart Contract Vulnerabilities
Cuong Tran Manh, Hieu Dinh Vo
While smart contracts are foundational elements of blockchain applications, their inherent susceptibility to security vulnerabilities poses a significant challenge. Existing traini…
Reinforcement Learning-Based REST API Testing with Multi-Coverage
Tien-Quang Nguyen, Nghia-Hieu Cong, Ngoc-Minh Quach +2
REST (Representational State Transfer) APIs have become integral for data communication and exchange due to their simplicity, scalability, and compatibility with web standards. How…
RAMBO: Enhancing RAG-based Repository-Level Method Body Completion
Tuan-Dung Bui, Duc-Thieu Luu-Van, Thanh-Phat Nguyen +3
Code completion is essential in software development, helping developers by predicting code snippets based on context. Among completion tasks, Method Body Completion (MBC) is parti…
An Empirical Study on Capability of Large Language Models in Understanding Code Semantics
Thu-Trang Nguyen, Thanh Trong Vu, Hieu Dinh Vo +1
Large Language Models for Code (code LLMs) have demonstrated remarkable performance across various software engineering (SE) tasks, increasing the application of code LLMs in softw…
Automated Description Generation for Software Patches
Thanh Trong Vu, Tuan-Dung Bui, Thanh-Dat Do +3
Software patches are pivotal in refining and evolving codebases, addressing bugs, vulnerabilities, and optimizations. Patch descriptions provide detailed accounts of changes, aidin…
Silent Vulnerability-fixing Commit Identification Based on Graph Neural Networks
Hieu Dinh Vo, Thanh Trong Vu, Son Nguyen
The growing dependence of software projects on external libraries has generated apprehensions regarding the security of these libraries because of concealed vulnerabilities. Handli…
VFFINDER: A Graph-based Approach for Automated Silent Vulnerability-Fix Identification
Son Nguyen, Thanh Trong Vu, Hieu Dinh Vo
The increasing reliance of software projects on third-party libraries has raised concerns about the security of these libraries due to hidden vulnerabilities. Managing these vulner…
Can An Old Fashioned Feature Extraction and A Light-weight Model Improve Vulnerability Type Identification Performance?
Hieu Dinh Vo, Son Nguyen
Recent advances in automated vulnerability detection have achieved potential results in helping developers determine vulnerable components. However, after detecting vulnerabilities…
Context-Encoded Code Change Representation for Automated Commit Message Generation
Thanh Trong Vu, Thanh-Dat Do, Hieu Dinh Vo
Changes in source code are an inevitable part of software development. They are the results of indispensable activities such as fixing bugs or improving functionality. Descriptions…
ARIST: An Effective API Argument Recommendation Approach
Son Nguyen, Cuong Tran Manh, Kien T. Tran +4
Learning and remembering to use APIs are difficult. Several techniques have been proposed to assist developers in using APIs. Most existing techniques focus on recommending the rig…
Code-centric Learning-based Just-In-Time Vulnerability Detection
Son Nguyen, Thu-Trang Nguyen, Thanh Trong Vu +3
Attacks against computer systems exploiting software vulnerabilities can cause substantial damage to the cyber-infrastructure of our modern society and economy. To minimize the con…
Using Multiple Code Representations to Prioritize Static Analysis Warnings
Thanh Trong Vu, Hieu Dinh Vo
In order to ensure the quality of software and prevent attacks from hackers on critical systems, static analysis tools are frequently utilized to detect vulnerabilities in the earl…
Ranking Warnings of Static Analysis Tools Using Representation Learning
Kien-Tuan Ngo, Dinh-Truong Do, Thu-Trang Nguyen +1
Static analysis tools are frequently used to detect potential vulnerabilities in software systems. However, an inevitable problem of these tools is their large number of warnings w…
A Variability Fault Localization Approach for Software Product Lines
Thu-Trang Nguyen, Kien-Tuan Ngo, Son Nguyen +1
Software fault localization is one of the most expensive, tedious, and time-consuming activities in program debugging. This activity becomes even much more challenging in Software…
Variability Fault Localization: A Benchmark
Kien-Tuan Ngo, Thu-Trang Nguyen, Son Nguyen +1
Software fault localization is one of the most expensive, tedious, and time-consuming activities in program debugging. This activity becomes even much more challenging in Software…