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Information
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Software Applications Programming and Data Security
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Software Applications Programming and Data Security

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information and Communications Technology".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 1535

Special Issue Editors

Dr. Todor Todorov

E-Mail Website
Guest Editor
Faculty of Mathematics and Informatics, St. Cyril and St. Methodius University of Veliko Tarnovo, 5000 Veliko Tarnovo, Bulgaria
Interests: software application programming; web programming; data security; coding theory
Dr. Galina Bogdanova

E-Mail Website
Guest Editor
Department of Software Engineering and Information Systems, Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Sofia, Bulgaria
Interests: codes theory; data bases; data protection; watermarking
Special Issues, Collections and Topics in MDPI journals
Dr. Malinka Spasova Ivanova

E-Mail Website
Guest Editor
Faculty of Applied Mathematics and Informatics, Department of Informatics, Technical University of Sofia, Sofia, Bulgaria
Interests: security and privacy; industrial informatics; technology-enhanced learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

There are numerous current scientific challenges in the field of software development architectures and programming technologies. Theoretical and practical aspects of data security are also an important part of information and communication technologies.

This Special Issue addresses various aspects of the software development process, including its specific phases and activities. It also explores topics beyond process structure, such as automation, tool support, management, data analysis, and decision-making aids. This Special Issue aims to showcase theoretical advancements and their practical applications.

This Special Issue aims to present methods and tools in software engineering related to developing secure software applications and preventing malicious activities in cyber computer systems.

We invite authors to submit original research papers and high-quality review articles on software applications programming, data security, and all topics related to them.

Dr. Todor Todorov
Dr. Galina Bogdanova
Dr. Malinka Ivanova
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. Information is an international peer-reviewed open access monthly 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 1800 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

  • agile software development
  • design patterns
  • programming languages
  • software engineering tools
  • software testing
  • application security and privacy
  • software security
  • data protection

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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

34 pages, 15469 KB  
Article
Innovating Intrusion Detection Classification Analysis for an Imbalanced Data Sample
by Elrasheed Ismail Mohommoud Zayid, Ibrahim Isah, Abdulmalik A. Humayed and Yagoub Abbker Adam
Information 2025, 16(10), 883; https://doi.org/10.3390/info16100883 - 10 Oct 2025
Viewed by 363
Abstract
This work is designed to assist researchers and interested learners in comprehending and putting deep machine learning classification approaches into practice. It aimed to simplify, facilitate, and advance classification methodology skills. To make it easier for the users to understand, it employed a [...] Read more.
This work is designed to assist researchers and interested learners in comprehending and putting deep machine learning classification approaches into practice. It aimed to simplify, facilitate, and advance classification methodology skills. To make it easier for the users to understand, it employed a methodical approach. The categorization assessment measures seek to give the fundamentals of these measures and demonstrate how they operate to function as a comprehensive resource for academics interested in this area. Intrusion detection and threat analysis (IDAT) is a particularly unpleasant cybersecurity issue. In this study, IDAT is identified as a case study, and a real-sample dataset that was used for institutional and community awareness was generated by the researchers. This review shows that, to solve a classification problem, it is crucial to use the output of classification in terms of performance measurements, encompassing both conventional criteria and contemporary metrics. This study focused on addressing the dynamic of classification assessment capabilities for using both scalars and visual metrics, and to fix imbalanced dataset difficulties. In conclusion, this review is a useful tool for researchers, especially when they are working on big data preprocessing, handling imbalanced data for multiclass assessment, and ML classification. Full article
(This article belongs to the Special Issue Software Applications Programming and Data Security)
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26 pages, 2266 KB  
Article
A Phrase Fill-in-Blank Problem in a Client-Side Web Programming Assistant System
by Huiyu Qi, Zhikang Li, Nobuo Funabiki, Htoo Htoo Sandi Kyaw and Wen Chung Kao
Information 2025, 16(8), 709; https://doi.org/10.3390/info16080709 - 20 Aug 2025
Viewed by 677
Abstract
Mastering client-side Web programming is essential for the development of responsive and interactive Web applications. To support novice students’ self-study, in this paper, we propose a novel exercise format called the phrase fill-in-blank problem (PFP) in the Web Programming Learning Assistant System (WPLAS) [...] Read more.
Mastering client-side Web programming is essential for the development of responsive and interactive Web applications. To support novice students’ self-study, in this paper, we propose a novel exercise format called the phrase fill-in-blank problem (PFP) in the Web Programming Learning Assistant System (WPLAS). A PFP instance presents a source code with blanked phrases (a set of elements) and corresponding Web page screenshots. Then, it requests the user to fill in the blanks, and the answers are automatically evaluated through string matching with predefined correct answers. By increasing blanks, PFP can come close to writing a code from scratch. To facilitate scalable and context-aware question creation, we implemented the PFP instance generation algorithm in Python using regular expressions. This approach targets meaningful code segments in HTML, CSS, and JavaScript that reflect the interactive behavior of front-end development. For evaluations, we generated 10 PFP instances for basic Web programming topics and 5 instances for video games and assigned them to students at Okayama University, Japan, and the State Polytechnic of Malang, Indonesia. Their solution results show that most students could solve them correctly, indicating the effectiveness and accessibility of the generated instances. In addition, we investigated the ability of generative AI, specifically ChatGPT, to solve the PFP instances. The results show 86.7% accuracy for basic-topic PFP instances. Although it still cannot fully find answers, we must monitor progress carefully. In future work, we will enhance PFP in WPLAS to handle non-unique answers by improving answer validation for flexible recognition of equivalent responses. Full article
(This article belongs to the Special Issue Software Applications Programming and Data Security)
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