Van-Viet Nguyen¹, Huu-Khanh Nguyen², Kim-Son Nguyen¹, Thi Minh-Hue Luong¹, Duc-Quang Vu¹, Trung-Nghia Phung³, The-Vinh Nguyen^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.075442 - 29 January 2026

Abstract It remains difficult to automate the creation and validation of Unified Modeling Language (UML) diagrams due to unstructured requirements, limited automated pipelines, and the lack of reliable evaluation methods. This study introduces a cohesive architecture that amalgamates requirement development, UML synthesis, and multimodal validation. First, LLaMA-3.2-1B-Instruct was utilized to generate user-focused requirements. Then, DeepSeek-R1-Distill-Qwen-32B applies its reasoning skills to transform these requirements into PlantUML code. Using this dual-LLM pipeline, we constructed a synthetic dataset of 11,997 UML diagrams spanning six major diagram families. Rendering analysis showed that 89.5% of the generated diagrams compile correctly, while… More >

Anis Zarrad¹, Thomas Armstrong², Jaber Jemai^3,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072768 - 12 January 2026

Abstract Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability. While prioritization selects the most relevant test cases for optimal coverage, ranking further refines their execution order to detect critical faults earlier. This study investigates machine learning techniques to enhance both prioritization and ranking, contributing to more effective and efficient testing processes. We first employ advanced feature engineering alongside ensemble models, including Gradient Boosted, Support Vector Machines, Random Forests, and Naive Bayes classifiers to optimize test case prioritization, achieving an accuracy score of 0.98847… More >

Manaliben Amin^*

Journal on Artificial Intelligence, Vol.8, pp. 1-18, 2026, DOI:10.32604/jai.2026.073895 - 07 January 2026

Abstract Artificial intelligence (AI) is steadily making its way into pharmaceutical validation, where it promises faster documentation, smarter testing strategies, and better handling of deviations. These gains are attractive, but in a regulated environment speed is never enough. Regulators want assurance that every system is reliable, that decisions are explainable, and that human accountability remains central. This paper sets out a Human-in-the-Loop (HITL) AI approach for Computer System Validation (CSV) and Computer Software Assurance (CSA). It relies on explainable AI (XAI) tools but keeps structured human review in place, so automation can be used without creating… More >

Traian-Radu Ploscă^1,*, Alexandru-Mihai Pescaru², Bianca-Valeria Rus¹, Daniel-Ioan Curiac^1,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.069707 - 10 November 2025

Abstract Objective expertise evaluation of individuals, as a prerequisite stage for team formation, has been a long-term desideratum in large software development companies. With the rapid advancements in machine learning methods, based on reliable existing data stored in project management tools’ datasets, automating this evaluation process becomes a natural step forward. In this context, our approach focuses on quantifying software developer expertise by using metadata from the task-tracking systems. For this, we mathematically formalize two categories of expertise: technology-specific expertise, which denotes the skills required for a particular technology, and general expertise, which encapsulates overall knowledge More >

Xiaohu Song¹, Zhiliang Zhu^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-19, 2026, DOI:10.32604/cmc.2025.068930 - 10 November 2025

Abstract Vulnerabilities are a known problem in modern Open Source Software (OSS). Most developers often rely on third-party libraries to accelerate feature implementation. However, these libraries may contain vulnerabilities that attackers can exploit to propagate malicious code, posing security risks to dependent projects. Existing research addresses these challenges through Software Composition Analysis (SCA) for vulnerability detection and remediation. Nevertheless, current solutions may introduce additional issues, such as incompatibilities, dependency conflicts, and additional vulnerabilities. To address this, we propose Vulnerability Scan and Protection (), a robust solution for detection and remediation vulnerabilities in Java projects. Specifically, builds… More >

Nader Karmous¹, Wadii Jlassi¹, Mohamed Ould-Elhassen Aoueileyine¹, Imen Filali^2,*, Ridha Bouallegue¹

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4363-4393, 2025, DOI:10.32604/cmes.2025.074178 - 23 December 2025

Abstract This paper introduces a robust Distributed Denial-of-Service attack detection framework tailored for Software-Defined Networking based Internet of Things environments, built upon a novel, synthetic multi-vector dataset generated in a Mininet-Ryu testbed using real-time flow-based labeling. The proposed model is based on the XGBoost algorithm, optimized with Principal Component Analysis for dimensionality reduction, utilizing lightweight flow-level features extracted from OpenFlow statistics to classify attacks across critical IoT protocols including TCP, UDP, HTTP, MQTT, and CoAP. The model employs lightweight flow-level features extracted from OpenFlow statistics to ensure low computational overhead and fast processing. Performance was rigorously… More >

Mekhled Alharbi^1,*, Khalid Haseeb², Mamoona Humayun^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2601-2616, 2025, DOI:10.32604/cmes.2025.073039 - 26 November 2025

Abstract Emerging technologies and the Internet of Things (IoT) are integrating for the growth and development of heterogeneous networks. These systems are providing real-time devices to end users to deliver dynamic services and improve human lives. Most existing approaches have been proposed to improve energy efficiency and ensure reliable routing; however, trustworthiness and network scalability remain significant research challenges. In this research work, we introduce an AI-enabled Software-Defined Network (SDN)- driven framework to provide secure communication, trusted behavior, and effective route maintenance. By considering multiple parameters in the forwarder selection process, the proposed framework enhances network More >

Khawaja Tahir Mehmood^1,*, Muhammad Majid Hussain²

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5643-5670, 2025, DOI:10.32604/cmc.2025.068404 - 23 October 2025

Abstract In the modern era of 5th generation (5G) networks, the data generated by User Equipments (UE) has increased significantly, with data file sizes varying from modest sensor logs to enormous multimedia files. In modern telecommunications networks, the need for high-end security and efficient management of these large data files is a great challenge for network designers. The proposed model provides the efficient real-time virtual data storage of UE data files (light and heavy) using an object storage system MinIO having inbuilt Software Development Kits (SDKs) that are compatible with Amazon (S3) Application Program Interface (API)… More >

Brian Mweu^1,*, John Ndia²

Journal of Cyber Security, Vol.7, pp. 417-437, 2025, DOI:10.32604/jcs.2025.071765 - 10 October 2025

Abstract Static analysis methods are crucial in developing secure software, as they allow for the early identification of vulnerabilities before the software is executed. This systematic review follows Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines to assess static analysis techniques for software security enhancement. We systematically searched IEEE Xplore, Association for Computing Machinery (ACM) Digital Library, SpringerLink, and ScienceDirect for journal articles published between 2017 and 2025. The review examines hybrid analyses and machine learning integration to enhance vulnerability detection accuracy. Static analysis tools enable early fault detection but face persistent challenges. More >

Elyson De La Cruz^*, Hanh Le, Karthik Meduri, Geeta Sandeep Nadella^*, Hari Gonaygunta

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3569-3582, 2025, DOI:10.32604/cmc.2025.068137 - 23 September 2025

Abstract The rapid integration of artificial intelligence (AI) into software development, driven by large language models (LLMs), is reshaping the role of programmers from traditional coders into strategic collaborators within Industry 4.0 ecosystems. This qualitative study employs a hermeneutic phenomenological approach to explore the lived experiences of Information Technology (IT) professionals as they navigate a dynamic technological landscape marked by intelligent automation, shifting professional identities, and emerging ethical concerns. Findings indicate that developers are actively adapting to AI-augmented environments by engaging in continuous upskilling, prompt engineering, interdisciplinary collaboration, and heightened ethical awareness. However, participants also voiced… More >