145 citations · 347 across the 20 of their papers we have counts for
20 papers
Software Vulnerability Management in the Era of Artificial Intelligence: An Industry Perspective
M. Mehdi Kholoosi, Triet Huynh Minh Le, M. Ali Babar
Artificial Intelligence (AI) has revolutionized software development, particularly by automating repetitive tasks and improving developer productivity. While these advancements are…
LLMSecConfig: An LLM-Based Approach for Fixing Software Container Misconfigurations
Ziyang Ye, Triet Huynh Minh Le, M. Ali Babar
Security misconfigurations in Container Orchestrators (COs) can pose serious threats to software systems. While Static Analysis Tools (SATs) can effectively detect these security v…
Automated Code-centric Software Vulnerability Assessment: How Far Are We? An Empirical Study in C/C++
Anh The Nguyen, Triet Huynh Minh Le, M. Ali Babar
Background: The C and C++ languages hold significant importance in Software Engineering research because of their widespread use in practice. Numerous studies have utilized Machine…
Automatic Data Labeling for Software Vulnerability Prediction Models: How Far Are We?
Triet H. M. Le, M. Ali Babar
Background: Software Vulnerability (SV) prediction needs large-sized and high-quality data to perform well. Current SV datasets mostly require expensive labeling efforts by experts…
Mitigating Data Imbalance for Software Vulnerability Assessment: Does Data Augmentation Help?
Triet H. M. Le, M. Ali Babar
Background: Software Vulnerability (SV) assessment is increasingly adopted to address the ever-increasing volume and complexity of SVs. Data-driven approaches have been widely used…
Systematic Literature Review on Application of Learning-based Approaches in Continuous Integration
Ali Kazemi Arani, Triet Huynh Minh Le, Mansooreh Zahedi +1
Context: Machine learning (ML) and deep learning (DL) analyze raw data to extract valuable insights in specific phases. The rise of continuous practices in software projects emphas…
Software Vulnerability Prediction in Low-Resource Languages: An Empirical Study of CodeBERT and ChatGPT
Triet H. M. Le, M. Ali Babar, Tung Hoang Thai
Background: Software Vulnerability (SV) prediction in emerging languages is increasingly important to ensure software security in modern systems. However, these languages usually h…
Are Latent Vulnerabilities Hidden Gems for Software Vulnerability Prediction? An Empirical Study
Triet H. M. Le, Xiaoning Du, M. Ali Babar
Collecting relevant and high-quality data is integral to the development of effective Software Vulnerability (SV) prediction models. Most of the current SV datasets rely on SV-fixi…
Mitigating ML Model Decay in Continuous Integration with Data Drift Detection: An Empirical Study
Ali Kazemi Arani, Triet Huynh Minh Le, Mansooreh Zahedi +1
Background: Machine Learning (ML) methods are being increasingly used for automating different activities, e.g., Test Case Prioritization (TCP), of Continuous Integration (CI). How…
Systematic Literature Review on Application of Machine Learning in Continuous Integration
Ali Kazemi Arani, Triet Huynh Minh Le, Mansooreh Zahedi +1
This research conducted a systematic review of the literature on machine learning (ML)-based methods in the context of Continuous Integration (CI) over the past 22 years. The study…
SoK: Machine Learning for Continuous Integration
Ali Kazemi Arani, Mansooreh Zahedi, Triet Huynh Minh Le +1
Continuous Integration (CI) has become a well-established software development practice for automatically and continuously integrating code changes during software development. An…
Towards an Improved Understanding of Software Vulnerability Assessment Using Data-Driven Approaches
Triet H. M. Le
The thesis advances the field of software security by providing knowledge and automation support for software vulnerability assessment using data-driven approaches. Software vulner…
On the Use of Fine-grained Vulnerable Code Statements for Software Vulnerability Assessment Models
Triet H. M. Le, M. Ali Babar
Many studies have developed Machine Learning (ML) approaches to detect Software Vulnerabilities (SVs) in functions and fine-grained code statements that cause such SVs. However, th…
Automated Security Assessment for the Internet of Things
Xuanyu Duan, Mengmeng Ge, Triet H. M. Le +4
Internet of Things (IoT) based applications face an increasing number of potential security risks, which need to be systematically assessed and addressed. Expert-based manual asses…
DeepCVA: Automated Commit-level Vulnerability Assessment with Deep Multi-task Learning
Triet H. M. Le, David Hin, Roland Croft +1
It is increasingly suggested to identify Software Vulnerabilities (SVs) in code commits to give early warnings about potential security risks. However, there is a lack of effort to…
A Survey on Data-driven Software Vulnerability Assessment and Prioritization
Triet H. M. Le, Huaming Chen, M. Ali Babar
Software Vulnerabilities (SVs) are increasing in complexity and scale, posing great security risks to many software systems. Given the limited resources in practice, SV assessment…
Automated Software Vulnerability Assessment with Concept Drift
Triet H. M. Le, Bushra Sabir, M. Ali Babar
Software Engineering researchers are increasingly using Natural Language Processing (NLP) techniques to automate Software Vulnerabilities (SVs) assessment using the descriptions in…
A Large-scale Study of Security Vulnerability Support on Developer Q&A Websites
Triet H. M. Le, Roland Croft, David Hin +1
Context: Security Vulnerabilities (SVs) pose many serious threats to software systems. Developers usually seek solutions to addressing these SVs on developer Question and Answer (Q…
PUMiner: Mining Security Posts from Developer Question and Answer Websites with PU Learning
Triet H. M. Le, David Hin, Roland Croft +1
Security is an increasing concern in software development. Developer Question and Answer (Q&A) websites provide a large amount of security discussion. Existing studies have used hu…
Deep Learning for Source Code Modeling and Generation: Models, Applications and Challenges
Triet H. M. Le, Hao Chen, M. Ali Babar
Deep Learning (DL) techniques for Natural Language Processing have been evolving remarkably fast. Recently, the DL advances in language modeling, machine translation and paragraph…