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Advanced Technologies in Intelligent Software Methodologies, Tools, and Techniques
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Advanced Technologies in Intelligent Software Methodologies, Tools, and Techniques

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 March 2026 | Viewed by 8854

Special Issue Editors

Prof. Dr. Hamido Fujita

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Guest Editor
Regional Research Center, Iwate Prefectural University, Iwate 020-8550, Japan
Interests: applied intelligence; machine learning for health care; granular computing; health care prediction; three-way decision
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Héctor Manuel Pérez-Meana

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Guest Editor
Instituto Politécnico Nacional, Av. Luis Enrique Erro S/N, Unidad Profesional Adolfo López Mateos, Zacatenco, Alcaldía Gustavo A. Madero, Ciudad de México 07738, Mexico
Interests: compressive sensing; speech recognition; digital watermarking; data hiding; speech processing; digital image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Andres Hernandez-Matamoros

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Guest Editor
Department of Frontier Media Science, Meiji University, Tokyo 164-8525, Japan
Interests: data privacy; machine learning; deep learning; image processing; anomaly detection; processing digital signals
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The SOMET conference highlights and reflects the state of the art and new trends in software methodologies, tools, and techniques. You are invited to participate to help build a forum for exchanging ideas and experiences to foster new directions in software development methodologies and related tools and techniques. This conference is focused on exploring innovations, controversies, and challenges facing the software engineering community today. The conference brings together theory and experience to propose and evaluate solutions to software engineering problems. The conference also provides a forum and an opportunity to assess the current state of the art in intelligent software techniques and to chart software science initiated from experience to theory. This conference is an opportunity for the software science community to think about where we are today and where we are going. The Special Issue is in cooperation with the conference SOMET 2024 (https://atenea.esimecu.ipn.mx/; https://www.i-somet.org/somet2024/) and welcomes submissions from participants of the conference.

Prof. Dr. Hamido Fujita
Prof. Dr. Héctor Manuel Pérez-Meana
Dr. Andres Hernandez-Matamoros
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • software methodologies
  • software developments
  • automatic software generation
  • intelligent software systems
  • software security
  • information security
  • medical informatics
  • artificial intelligence technology
  • bioinformatics
  • data hiding
  • speech processing
  • digital image processing

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Published Papers (7 papers)

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Research

15 pages, 4502 KB  
Article
A Comprehensive QR Code Protection and Recovery System Using Secure Encryption, Chromatic Multiplexing, and Wavelength-Based Decoding
by Paola Noemi San Agustin-Crescencio, Leobardo Hernandez-Gonzalez, Pedro Guevara-Lopez, Oswaldo Ulises Juarez-Sandoval, Jazmin Ramirez-Hernandez and Eduardo Salvador Estevez-Encarnacion
Appl. Sci. 2025, 15(17), 9708; https://doi.org/10.3390/app15179708 - 3 Sep 2025
Viewed by 419
Abstract
QR codes (Quick Response) were originally developed by the automotive industry to enable rapid data exchange and have since evolved into versatile tools for commercial applications, such as linking to products or websites. However, the scope of their adoption has expanded into sensitive [...] Read more.
QR codes (Quick Response) were originally developed by the automotive industry to enable rapid data exchange and have since evolved into versatile tools for commercial applications, such as linking to products or websites. However, the scope of their adoption has expanded into sensitive domains including financial, corporate, and governmental sectors. In order to address increasing security concerns, this work proposes a novel three-layer protection scheme. First, data confidentiality is ensured through encryption—in this study, symmetric AES (Advanced Encryption Standard) encryption is used as an example, though any encryption algorithm can be employed. Second, a multiplexing technique is employed to integrate two independent dichromatic QR codes into a single printed chromatic structure. Third, the recovery of each dichromatic code is achieved through the controlled incidence of specific wavelengths, not only providing improved channel separation but also functioning as a physical access control mechanism. This physical layer restricts unauthorized reading. Full article
(This article belongs to the Special Issue Advanced Technologies in Intelligent Software Methodologies, Tools, and Techniques)
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22 pages, 4036 KB  
Article
An Online Modular Framework for Anomaly Detection and Multiclass Classification in Video Surveillance
by Jonathan Flores-Monroy, Gibran Benitez-Garcia, Mariko Nakano-Miyatake and Hiroki Takahashi
Appl. Sci. 2025, 15(17), 9249; https://doi.org/10.3390/app15179249 - 22 Aug 2025
Viewed by 389
Abstract
Video surveillance systems are a key tool for the identification of anomalous events, but they still rely heavily on human analysis, which limits their efficiency. Current video anomaly detection models aim to automatically detect such events. However, most of them provide only a [...] Read more.
Video surveillance systems are a key tool for the identification of anomalous events, but they still rely heavily on human analysis, which limits their efficiency. Current video anomaly detection models aim to automatically detect such events. However, most of them provide only a binary classification (normal or anomalous) and do not identify the specific type of anomaly. Although recent proposals address anomaly classification, they typically require full video analysis, making them unsuitable for online applications. In this work, we propose a modular framework for the joint detection and classification of anomalies, designed to operate on individual clips within continuous video streams. The architecture integrates interchangeable modules (feature extractor, detector, and classifier) and is adaptable to both offline and online scenarios. Specifically, we introduce a multi-category classifier that processes only anomalous clips, enabling efficient clip-level classification. Experiments conducted on the UCF-Crime dataset validate the effectiveness of the framework, achieving 74.77% clip-level accuracy and 58.96% video-level accuracy, surpassing prior approaches and confirming its applicability in real-world surveillance environments. Full article
(This article belongs to the Special Issue Advanced Technologies in Intelligent Software Methodologies, Tools, and Techniques)
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31 pages, 3464 KB  
Article
An Intelligent Method for C++ Test Case Synthesis Based on a Q-Learning Agent
by Serhii Semenov, Oleksii Kolomiitsev, Mykhailo Hulevych, Patryk Mazurek and Olena Chernyk
Appl. Sci. 2025, 15(15), 8596; https://doi.org/10.3390/app15158596 - 2 Aug 2025
Viewed by 465
Abstract
Ensuring software quality during development requires effective regression testing. However, test suites in open-source libraries often grow large, redundant, and difficult to maintain. Most traditional test suite optimization methods treat test cases as atomic units, without analyzing the utility of individual instructions. This [...] Read more.
Ensuring software quality during development requires effective regression testing. However, test suites in open-source libraries often grow large, redundant, and difficult to maintain. Most traditional test suite optimization methods treat test cases as atomic units, without analyzing the utility of individual instructions. This paper presents an intelligent method for test case synthesis using a Q-learning agent. The agent learns to construct compact test cases by interacting with an execution environment and receives rewards based on branch coverage improvements and simultaneous reductions in test case length. The training process includes a pretraining phase that transfers knowledge from the original test suite, followed by adaptive learning episodes on individual test cases. As a result, the method requires no formal documentation or API specifications and uses only execution traces of the original test cases. An explicit synthesis algorithm constructs new test cases by selecting API calls from a learned policy encoded in a Q-table. Experiments were conducted on two open-source C++ libraries of differing API complexity and original test suite size. The results show that the proposed method can reach up to 67% test suite reduction while preserving branch coverage, confirming its effectiveness for regression test suite minimization in resource-constrained or specification-limited environments. Full article
(This article belongs to the Special Issue Advanced Technologies in Intelligent Software Methodologies, Tools, and Techniques)
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20 pages, 3000 KB  
Article
NRNH-AR: A Small Robotic Agent Using Tri-Fold Learning for Navigation and Obstacle Avoidance
by Carlos Vasquez-Jalpa, Mariko Nakano, Martin Velasco-Villa and Osvaldo Lopez-Garcia
Appl. Sci. 2025, 15(15), 8149; https://doi.org/10.3390/app15158149 - 22 Jul 2025
Viewed by 383
Abstract
We propose a tri-fold learning algorithm, called Neuroevolution of Hybrid Neural Networks in a Robotic Agent (acronym in Spanish, NRNH-AR), based on deep reinforcement learning (DRL), with self-supervised learning (SSL) and unsupervised learning (USL) steps, specifically designed to be implemented in a small [...] Read more.
We propose a tri-fold learning algorithm, called Neuroevolution of Hybrid Neural Networks in a Robotic Agent (acronym in Spanish, NRNH-AR), based on deep reinforcement learning (DRL), with self-supervised learning (SSL) and unsupervised learning (USL) steps, specifically designed to be implemented in a small autonomous navigation robot capable of operating in constrained physical environments. The NRNH-AR algorithm is designed for a small physical robotic agent with limited resources. The proposed algorithm was evaluated in four critical aspects: computational cost, learning stability, required memory size, and operation speed. The results obtained show that the performance of NRNH-AR is within the ranges of the Deep Q Network (DQN), Deep Deterministic Policy Gradient (DDPG), and Twin Delayed Deep Deterministic Policy Gradient (TD3). The proposed algorithm comprises three types of learning algorithms: SSL, USL, and DRL. Thanks to the series of learning algorithms, the proposed algorithm optimizes the use of resources and demonstrates adaptability in dynamic environments, a crucial aspect of navigation robotics. By integrating computer vision techniques based on a Convolutional Neuronal Network (CNN), the algorithm enhances its abilities to understand visual observations of the environment rapidly and detect a specific object, avoiding obstacles. Full article
(This article belongs to the Special Issue Advanced Technologies in Intelligent Software Methodologies, Tools, and Techniques)
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19 pages, 13635 KB  
Article
IPN HandS: Efficient Annotation Tool and Dataset for Skeleton-Based Hand Gesture Recognition
by Gibran Benitez-Garcia, Jesus Olivares-Mercado, Gabriel Sanchez-Perez and Hiroki Takahashi
Appl. Sci. 2025, 15(11), 6321; https://doi.org/10.3390/app15116321 - 4 Jun 2025
Viewed by 1223
Abstract
Hand gesture recognition (HGR) heavily relies on high-quality annotated datasets. However, annotating hand landmarks in video sequences is a time-intensive challenge. In this work, we introduce IPN HandS, an enhanced version of our IPN Hand dataset, which now includes approximately 700,000 hand skeleton [...] Read more.
Hand gesture recognition (HGR) heavily relies on high-quality annotated datasets. However, annotating hand landmarks in video sequences is a time-intensive challenge. In this work, we introduce IPN HandS, an enhanced version of our IPN Hand dataset, which now includes approximately 700,000 hand skeleton annotations and corrected gesture boundaries. To generate these annotations efficiently, we propose a novel annotation tool that combines automatic detection, inter-frame interpolation, copy–paste capabilities, and manual refinement. This tool significantly reduces annotation time from 70 min to just 27 min per video, allowing for the scalable and precise annotation of large datasets. We validate the advantages of the IPN HandS dataset by training a lightweight LSTM-based model using these annotations and comparing its performance against models trained with annotations from the widely used MediaPipe hand pose estimators. Our model achieves an accuracy that is 12% higher than the MediaPipe Hands model and 8% higher than the MediaPipe Holistic model. These results underscore the importance of annotation quality in training generalization and overall recognition performance. Both the IPN HandS dataset and the annotation tool will be released to support reproducible research and future work in HGR and related fields. Full article
(This article belongs to the Special Issue Advanced Technologies in Intelligent Software Methodologies, Tools, and Techniques)
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20 pages, 1777 KB  
Article
Improvements for the Planning Process in the Scrum Method
by Miroslav Žáček, Adéla Hamplová, Jan Tyrychtr and Ivan Vrana
Appl. Sci. 2025, 15(1), 202; https://doi.org/10.3390/app15010202 - 29 Dec 2024
Viewed by 2284
Abstract
In today’s dynamic development environments, agile methodologies like Scrum are essential for effective software project management. Despite its popularity, the Scrum framework’s reliance on subjective intuition during the sprint planning process can lead to inconsistencies and project delays. This study aims to enhance [...] Read more.
In today’s dynamic development environments, agile methodologies like Scrum are essential for effective software project management. Despite its popularity, the Scrum framework’s reliance on subjective intuition during the sprint planning process can lead to inconsistencies and project delays. This study aims to enhance the sprint planning phase by integrating the BeCoMe method, which is a mathematical approach designed to optimize task selection through structured compromise solutions. Utilizing a soft systems methodology, this research identifies and analyzes the existing inefficiencies in Scrum’s planning process. The implementation of the BeCoMe method in a real-world case study demonstrated significant improvements in task completion rates and overall project efficiency. The method’s structured process reduces biases, fosters team consensus, and enhances decision-making accuracy. The findings suggest that incorporating the BeCoMe method into Scrum can substantially mitigate risks, save time, and improve project outcomes by ensuring a more objective and data-driven approach to sprint planning. These insights are crucial for developers managing modern software projects, offering a robust framework for enhancing planning efficiency and success rates. Full article
(This article belongs to the Special Issue Advanced Technologies in Intelligent Software Methodologies, Tools, and Techniques)
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30 pages, 3635 KB  
Article
C2B: A Semantic Source Code Retrieval Model Using CodeT5 and Bi-LSTM
by Nazia Bibi, Ayesha Maqbool, Tauseef Rana, Farkhanda Afzal and Adnan Ahmed Khan
Appl. Sci. 2024, 14(13), 5795; https://doi.org/10.3390/app14135795 - 2 Jul 2024
Cited by 2 | Viewed by 2716
Abstract
To enhance the software implementation process, developers frequently leverage preexisting code snippets by exploring an extensive codebase. Existing code search tools often rely on keyword- or syntactic-based methods and struggle to fully grasp the semantics and intent behind code snippets. In this paper, [...] Read more.
To enhance the software implementation process, developers frequently leverage preexisting code snippets by exploring an extensive codebase. Existing code search tools often rely on keyword- or syntactic-based methods and struggle to fully grasp the semantics and intent behind code snippets. In this paper, we propose a novel hybrid C2B model that combines CodeT5 and bidirectional long short-term memory (Bi-LSTM) for source code search and recommendation. Our proposed C2B hybrid model leverages CodeT5’s domain-specific pretraining and Bi-LSTM’s contextual understanding to improve code representation and capture sequential dependencies. As a proof-of-concept application, we implemented the proposed C2B hybrid model as a deep neural code search tool and empirically evaluated the model on the large-scale dataset of CodeSearchNet. The experimental findings showcase that our methodology proficiently retrieves pertinent code snippets and surpasses the performance of prior state-of-the-art techniques. Full article
(This article belongs to the Special Issue Advanced Technologies in Intelligent Software Methodologies, Tools, and Techniques)
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