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Electronics
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Recent Advances of Software Engineering
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Recent Advances of Software Engineering

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 November 2024) | Viewed by 5130

Special Issue Editor

Dr. Arif Ali Khan

E-Mail Website
Guest Editor
Faculty of Information Technology and Electrical Engineering, University of Oulu, 90570 Oulu, Finland
Interests: quantum software engineering; software process improvement; multi-criteria decision analysis; DevOps; microservices architecture; AI ethics; agile software development; soft computing; evidence-based software engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue on "Recent Advances in Software Engineering" aims to showcase the latest developments, methodologies, and trends in the field of software engineering. As software systems become increasingly complex and integral to the functioning of global industries, the need for innovative solutions to design, develop, manage, and maintain these systems has never been more critical. This issue brings together pioneering research and cutting-edge studies that push the boundaries of what is possible in software engineering, highlighting new tools, techniques, and best practices.

In particular, this Special Issue focuses on the adaptation of software engineering practices to accommodate the rapid pace of technological advancements. Contributions explore the evolution of software development methodologies, the integration of artificial intelligence and machine learning in automated software development, and the increasing importance of cybersecurity measures in the software development lifecycle. Additionally, it explores the challenges and opportunities presented by the rise in quantum computing and how it is set to revolutionize software engineering paradigms.

Topics of interest for this Special Issue include, but are not limited to, the following:

  1. Agile and DevOps methodologies: Enhancements and innovative practices.
  2. Application of AI and machine learning in software development and maintenance.
  3. Cybersecurity practices in software engineering: Emerging threats and defense mechanisms.
  4. Quantum software engineering: Challenges and opportunities.
  5. Software engineering for cloud computing: Design, development, and deployment strategies.
  6. Human–computer interaction (HCI) in software development: Improving user experience through engineering.
  7. Sustainable software engineering: Practices for reducing environmental impact.
  8. Software testing and quality assurance: New tools and techniques for ensuring reliability and performance.

Through these topics, this Special Issue seeks to provide valuable insights and foster discussion among researchers, practitioners, and educators at the forefront of software engineering innovation.

Dr. Arif Ali Khan
Guest Editor

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. Electronics 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 engineering
  • DevOps methodologies
  • AI and machine learning in software development

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Published Papers (4 papers)

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Research

28 pages, 3778 KiB  
Article
Dermatological Health: A High-Performance, Embedded, and Distributed System for Real-Time Facial Skin Problem Detection
by Mehdi Pirahandeh
Electronics 2025, 14(7), 1319; https://doi.org/10.3390/electronics14071319 - 26 Mar 2025
Viewed by 216
Abstract
The real-time detection of facial skin problems is crucial for improving dermatological health, yet its practical implementation remains challenging. Early detection and timely intervention can significantly enhance skin health while reducing the financial burden associated with traditional dermatological treatments. This paper introduces EM-YOLO, [...] Read more.
The real-time detection of facial skin problems is crucial for improving dermatological health, yet its practical implementation remains challenging. Early detection and timely intervention can significantly enhance skin health while reducing the financial burden associated with traditional dermatological treatments. This paper introduces EM-YOLO, an advanced deep learning framework designed for embedded and distributed environments, leveraging improvements in YOLO models (versions 5, 7, and 8) for high-performance, real-time skin condition detection. The proposed architecture incorporates custom layers, including Squeeze-and-Excitation Block (SEB), Depthwise Separable Convolution (DWC), and Residual Dropout Block (RDB), to optimize feature extraction, enhance model robustness, and improve computational efficiency for deployment in resource-constrained settings. The proposed EM-YOLO model architecture clearly delineates the role of each architectural component, including preprocessing, detection, and postprocessing phases, ensuring a structured and modular representation of the detection pipeline. Extensive experiments demonstrate that EM-YOLO significantly outperforms traditional YOLO models in detecting facial skin conditions such as acne, dark circles, enlarged pores, and wrinkles. The proposed model achieves a precision of 82.30%, recall of 71.50%, F1-score of 76.40%, and mAP@0.5 of 68.80%, which are 23.52%, 32.7%, 29.34%, and 24.68% higher than standard YOLOv8, respectively. Furthermore, the enhanced YOLOv8 custom layers significantly improve system efficiency, achieving a request rate of 15 Req/s with an end-to-end latency of 0.315 s and an average processing latency of 0.021 s, demonstrating 51.61% faster inference and 200% improved throughput compared to traditional SCAS systems. These results highlight EM-YOLO’s superior precision, robustness, and efficiency, making it a highly effective solution for real-time dermatological detection tasks in embedded and distributed computing environments. Full article
(This article belongs to the Special Issue Recent Advances of Software Engineering)
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17 pages, 3245 KiB  
Article
Enhancing Security in Software Design Patterns and Antipatterns: A Framework for LLM-Based Detection
by Roberto Andrade, Jenny Torres and Iván Ortiz-Garcés
Electronics 2025, 14(3), 586; https://doi.org/10.3390/electronics14030586 - 1 Feb 2025
Viewed by 1207
Abstract
The detection of security vulnerabilities in software design patterns and antipatterns is crucial for maintaining robust and maintainable systems, particularly in dynamic Continuous Integration/Continuous Deployment (CI/CD) environments. Traditional static analysis tools, while effective for identifying isolated issues, often lack contextual awareness, leading to [...] Read more.
The detection of security vulnerabilities in software design patterns and antipatterns is crucial for maintaining robust and maintainable systems, particularly in dynamic Continuous Integration/Continuous Deployment (CI/CD) environments. Traditional static analysis tools, while effective for identifying isolated issues, often lack contextual awareness, leading to missed vulnerabilities and high rates of false positives. This paper introduces a novel framework leveraging Large Language Models (LLMs) to detect and mitigate security risks in design patterns and antipatterns. By analyzing relationships and behavioral dynamics in code, LLMs provide a nuanced, context-aware approach to identifying issues such as unauthorized state changes, insecure communication, and improper data handling. The proposed framework integrates key security heuristics—such as the principles of least privilege and input validation—to enhance LLM performance. An evaluation of the framework demonstrates its potential to outperform traditional tools in terms of accuracy and efficiency, enabling the proactive detection and remediation of vulnerabilities in real time. This study contributes to the field of software engineering by offering an innovative methodology for securing software systems using LLMs, promoting both academic research and practical application in industry settings. Full article
(This article belongs to the Special Issue Recent Advances of Software Engineering)
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19 pages, 716 KiB  
Article
Applying Large Language Model to User Experience Testing
by Nien-Lin Hsueh, Hsuen-Jen Lin and Lien-Chi Lai
Electronics 2024, 13(23), 4633; https://doi.org/10.3390/electronics13234633 - 24 Nov 2024
Viewed by 1678
Abstract
The maturation of internet usage environments has elevated User Experience (UX) to a critical factor in system success. However, traditional manual UX testing methods are hampered by subjectivity and lack of standardization, resulting in time-consuming and costly processes. This study explores the potential [...] Read more.
The maturation of internet usage environments has elevated User Experience (UX) to a critical factor in system success. However, traditional manual UX testing methods are hampered by subjectivity and lack of standardization, resulting in time-consuming and costly processes. This study explores the potential of Large Language Models (LLMs) to address these challenges by developing an automated UX testing tool. Our innovative approach integrates the Rapi web recording tool to capture user interaction data with the analytical capabilities of LLMs, utilizing Nielsen’s usability heuristics as evaluation criteria. This methodology aims to significantly reduce the initial costs associated with UX testing while maintaining assessment quality. To validate the tool’s efficacy, we conducted a case study featuring a tennis-themed course reservation system. The system incorporated multiple scenarios per page, allowing users to perform tasks based on predefined goals. We employed our automated UX testing tool to evaluate screenshots and interaction logs from user sessions. Concurrently, we invited participants to test the system and complete UX questionnaires based on their experiences. Comparative analysis revealed that varying prompts in the automated UX testing tool yielded different outcomes, particularly in detecting interface elements. Notably, our tool demonstrated superior capability in identifying issues aligned with Nielsen’s usability principles compared to participant evaluations. This research contributes to the field of UX evaluation by leveraging advanced language models and established usability heuristics. Our findings suggest that LLM-based automated UX testing tools can offer more consistent and comprehensive assessments. Full article
(This article belongs to the Special Issue Recent Advances of Software Engineering)
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16 pages, 620 KiB  
Article
Blockchain Handshaking with Software Assurance: Version++ Protocol for Bitcoin Cryptocurrency
by Arijet Sarker, Simeon Wuthier, Jinoh Kim, Jonghyun Kim and Sang-Yoon Chang
Electronics 2024, 13(19), 3857; https://doi.org/10.3390/electronics13193857 - 29 Sep 2024
Viewed by 957
Abstract
Cryptocurrency software implements cryptocurrency operations (including the distributed consensus protocol and peer-to-peer networking) and often involves the open-source community. We design a software assurance scheme for cryptocurrency and advance the cryptocurrency handshaking protocol by providing the verification capability of the Bitcoin software by [...] Read more.
Cryptocurrency software implements cryptocurrency operations (including the distributed consensus protocol and peer-to-peer networking) and often involves the open-source community. We design a software assurance scheme for cryptocurrency and advance the cryptocurrency handshaking protocol by providing the verification capability of the Bitcoin software by peers and preventing any potential peer from establishing a connection with modified Bitcoin software. Since we focus on Bitcoin (the most popular cryptocurrency) for implementation and integration, we call our scheme Version++, built on and advancing the current Bitcoin handshaking protocol based on the Version message. Our Version++ protocol providing software assurance is distinguishable from previous research because it is permissionless, distributed, and lightweight for its cryptocurrency application. Our scheme is permissionless since it does not require a centralized trusted authority (unlike the remote software attestation techniques from trusted computing); it is distributed since the peer checks the software assurances of its own peer connections; and it is designed for efficiency/lightweight to support the dynamic nature of the peer connections and large-scale broadcasting in cryptocurrency networking. Utilizing Merkle Tree for the efficiency of the proof verification, we implement and test Version++ on Bitcoin software and conduct experiments in an active Bitcoin node prototype connected to the Bitcoin Mainnet. Our prototype-based performance analyses demonstrate the lightweight design of Version++. The peer-specific verification grows logarithmically with the number of software files in processing time and in storage. Furthermore, the Version++ verification overhead is small compared to the version-verack handshaking process; we measure the overhead to be 0.524% in our local networking environment between virtual machines and between 0.057% and 0.282% (depending on the peer location) in our more realistic cloud-based experiments with remote peer machines. Full article
(This article belongs to the Special Issue Recent Advances of Software Engineering)
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