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Applied Sciences
Special Issues
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 July 2025 | Viewed by 2848

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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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

20 pages, 1777 KiB  
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 797
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)
►▼ Show Figures

Figure 1

Figure 1
<p>A problem in the Scrum planning process captured by Rich Picture.</p> Full article ">Figure 2
<p>Conceptual model.</p> Full article ">Figure 3
<p>Solving a problem in the sprint planning process using the BeCoMe implementation shown in Rich Picture.</p> Full article ">Figure 4
<p>Team members suggestions with BeCoMe result.</p> Full article ">Figure 5
<p>Graphical expression of the decision situation.</p> Full article ">
30 pages, 3635 KiB  
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 1 | Viewed by 1434
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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Figure 1

Figure 1
<p>Methodology for the proposed C2B hybrid model.</p> Full article ">Figure 2
<p>Proposed architecture for C2B hybrid model.</p> Full article ">Figure 3
<p>Training of C2B hybrid model.</p> Full article ">Figure 4
<p>Embedding learning code embeddings by minimizing cosine embedding loss.</p> Full article ">Figure 5
<p>Learning code embeddings by minimizing binary cross-entropy loss.</p> Full article ">Figure 6
<p>C2B hybrid model network architecture.</p> Full article ">Figure 7
<p>Total time taken to answer n queries. (<b>a</b>) CodeT5 time for n queries; (<b>b</b>) C2B hybrid model time for n queries.</p> Full article ">Figure 8
<p>Learning code embeddings by minimizing binary cross-entropy loss.</p> Full article ">
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