24 citations · 93 across the 26 of their papers we have counts for
30 papers
One Strategy Does Not Fit All: Rethinking Data Labeling for Just-in-Time Vulnerability Prediction
Quan Tran, Duong Nguyen, Thanh Le-Cong +4
Just-In-Time Vulnerability Prediction (JIT-VP) identifies security risks in code changes at commit time, enabling early feedback before vulnerabilities enter software systems. Howe…
Correct Code, Broken Contributions? SWE-CC: Benchmarking Repository Policy Compliance for Coding Agents
Hai Dang Truong, Rayner Goh, Thanh Le-Cong +1
Autonomous coding agents now resolve a substantial share of real-world GitHub issues. However, passing functional tests differs fundamentally from producing a high-quality contribu…
Athena: Vulnerability-Affected Library Identification via Knowledge Graph Completion
Phong Trinh Duy, Trang Dang Yen, Hung Nguyen-Huu +5
A single vulnerability in a widely used library can cascade through millions of dependent applications, yet more than half of vulnerability database entries contain missing or inco…
FPMoE: A Sparse Mixture-of-Experts Approach to Functional Code Generation
Loc Pham, Lang Hong Nguyet Anh, Thanh Le-Cong
Despite rapid progress in LLM-based code generation, existing models are predominantly trained on imperative languages, leaving functional programming languages (FPLs) such as Hask…
PatchGuru: Patch Oracle Inference from Natural Language Artifacts
Thanh Le-Cong, Bach Le, Toby Murray +2
As software systems evolve, patches may unintentionally alter program behavior. Validating patches against their intended semantics is difficult due to incomplete regression tests…
How Do Semantically Equivalent Code Transformations Impact Membership Inference on LLMs for Code?
Hua Yang, Alejandro Velasco, Thanh Le-Cong +3
The success of large language models for code relies on vast amounts of code data, including public open-source repositories, such as GitHub, and private, confidential code from co…
Perish or Flourish? A Holistic Evaluation of Large Language Models for Code Generation in Functional Programming
Nguyet-Anh H. Lang, Eric Lang, Thanh Le-Cong +2
Functional programming provides strong foundations for developing reliable and secure software systems, yet its adoption remains not widespread due to the steep learning curve. Rec…
Optimizing Spectral Signature in Code Backdoor Detection
Quoc Hung Le, Thanh Le-Cong, Bach Le +1
As Large Language Models (LLMs) are increasingly integrated into software development workflows, they also become prime targets for adversarial attacks. Among these, backdoor attac…
PatchSeeker: Mapping NVD Records to their Vulnerability-fixing Commits with LLM Generated Commits and Embeddings
Huu Hung Nguyen, Anh Tuan Nguyen, Thanh Le-Cong +8
Software vulnerabilities pose serious risks to modern software ecosystems. While the National Vulnerability Database (NVD) is the authoritative source for cataloging these vulnerab…
VulGuard: An Unified Tool for Evaluating Just-In-Time Vulnerability Prediction Models
Duong Nguyen, Manh Tran-Duc, Thanh Le-Cong +3
We present VulGuard, an automated tool designed to streamline the extraction, processing, and analysis of commits from GitHub repositories for Just-In-Time vulnerability prediction…
Toward Realistic Evaluations of Just-In-Time Vulnerability Prediction
Duong Nguyen, Thanh Le-Cong, Triet Huynh Minh Le +2
Modern software systems are increasingly complex, presenting significant challenges in quality assurance. Just-in-time vulnerability prediction (JIT-VP) is a proactive approach to…
From Empirical Evaluation to Context-Aware Enhancement: Repairing Regression Errors with LLMs
Anh Ho, Thanh Le-Cong, Bach Le +1
[...] Since then, various APR approaches, especially those leveraging the power of large language models (LLMs), have been rapidly developed to fix general software bugs. Unfortuna…
Mapping NVD Records to Their Vulnerability-fixing Commits: How Hard is It?
Huu Hung Nguyen, Ting Zhang, Duc Manh Tran +7
Mapping National Vulnerability Database (NVD) records to vulnerability-fixing commits (VFCs) is crucial for vulnerability analysis but challenging due to sparse explicit links in N…
Can LLMs Reason About Program Semantics? A Comprehensive Evaluation of LLMs on Formal Specification Inference
Thanh Le-Cong, Bach Le, Toby Murray
Large Language Models (LLMs) are increasingly being used to automate programming tasks. Yet, LLMs' capabilities in reasoning about program semantics are still inadequately studied,…
Memory-Efficient Large Language Models for Program Repair with Semantic-Guided Patch Generation
Thanh Le-Cong, Bach Le, Toby Murray
In this paper, we first show that increases in beam size, even for small-sized LLMs (1B-7B params), require extensive GPU usage, leading to up to 80% of recurring crashes due to me…
Comparison of Static Application Security Testing Tools and Large Language Models for Repo-level Vulnerability Detection
Xin Zhou, Duc-Manh Tran, Thanh Le-Cong +5
Software vulnerabilities pose significant security challenges and potential risks to society, necessitating extensive efforts in automated vulnerability detection. There are two po…
LEGION: Harnessing Pre-trained Language Models for GitHub Topic Recommendations with Distribution-Balance Loss
Yen-Trang Dang, Thanh-Le Cong, Phuc-Thanh Nguyen +4
Open-source development has revolutionized the software industry by promoting collaboration, transparency, and community-driven innovation. Today, a vast amount of various kinds of…
Towards Reliable Evaluation of Neural Program Repair with Natural Robustness Testing
Thanh Le-Cong, Dat Nguyen, Bach Le +1
In this paper, we propose shifting the focus of robustness evaluation for Neural Program Repair (NPR) techniques toward naturally-occurring data transformations. To accomplish this…
Are We Ready to Embrace Generative AI for Software Q&A?
Bowen Xu, Thanh-Dat Nguyen, Thanh Le-Cong +8
Stack Overflow, the world's largest software Q&A (SQA) website, is facing a significant traffic drop due to the emergence of generative AI techniques. ChatGPT is banned by Stack Ov…
Refining ChatGPT-Generated Code: Characterizing and Mitigating Code Quality Issues
Yue Liu, Thanh Le-Cong, Ratnadira Widyasari +4
We systematically study the quality of 4,066 ChatGPT-generated code implemented in two popular programming languages, i.e., Java and Python, for 2,033 programming tasks. The goal o…
Multi-Granularity Detector for Vulnerability Fixes
Truong Giang Nguyen, Thanh Le-Cong, Hong Jin Kang +9
With the increasing reliance on Open Source Software, users are exposed to third-party library vulnerabilities. Software Composition Analysis (SCA) tools have been created to alert…
PatchZero: Zero-Shot Automatic Patch Correctness Assessment
Xin Zhou, Bowen Xu, Kisub Kim +5
Automated Program Repair (APR) techniques have shown more and more promising results in fixing real-world bugs. Despite the effectiveness, APR techniques still face an overfitting…
CHRONOS: Time-Aware Zero-Shot Identification of Libraries from Vulnerability Reports
Yunbo Lyu, Thanh Le-Cong, Hong Jin Kang +5
Tools that alert developers about library vulnerabilities depend on accurate, up-to-date vulnerability databases which are maintained by security researchers. These databases recor…
Invalidator: Automated Patch Correctness Assessment via Semantic and Syntactic Reasoning
Thanh Le-Cong, Duc-Minh Luong, Xuan Bach D. Le +4
Automated program repair (APR) faces the challenge of test overfitting, where generated patches pass validation tests but fail to generalize. Existing methods for patch assessment…