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Applied Sciences
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Intelligent Software Engineering: Innovations, Challenges, and Applications
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Intelligent Software Engineering: Innovations, Challenges, and Applications

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

Deadline for manuscript submissions: 25 May 2025 | Viewed by 1260

Special Issue Editors

Dr. Mirko Perkusich

E-Mail Website
Guest Editor
Research, Development, and Innovation Centre (VIRTUS/UFCG), Federal University of Campina Grande, Campina Grande 58429-140, CEP, Brazil
Interests: software engineering
Prof. Dr. Emilia Mendes

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark
Interests: software engineering & computing systems

Special Issue Information

Dear Colleagues,

This Special Issue entitled "Intelligent Software Engineering: Innovations, Challenges, and Applications" explores the dynamic field of Intelligent Software Engineering (ISE). This rapidly evolving domain, at the intersection of artificial intelligence (AI) and software engineering, offers groundbreaking methodologies for designing, developing, and managing software systems. On one hand, ISE leverages AI-driven techniques like machine learning, data analytics, and intelligent decision-making to address complex software engineering challenges to improve efficiency, reliability, and functionality. On the other hand, it addresses the unique challenges of developing and maintaining AI-enabled systems.

This issue aims to highlight innovation in ISE, discussing the transformative impact of this field across various sectors, highlighting the latest research advancements, and identifying the practical applications and challenges practitioners face. It serves as a vital platform for experts in academia and industry to share their insights, foster collaborations, and shape the future trajectory of software engineering in an AI-dominated era.

Dr. Mirko Perkusich
Prof. Dr. Emilia Mendes
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

  • Artificial Intelligence in software engineering (AI4SE)
  • data mining in software engineering
  • DevOps for machine learning-enabled systems (MLOps)
  • Intelligent Software Engineering (ISE)
  • machine learning for software engineering (ML4SE)
  • reasoning under uncertainty in software engineering
  • recommender systems in software engineering
  • search-based software engineering
  • software analytics
  • software engineering for AI systems (SE2AI)

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Published Papers (1 paper)

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Research

38 pages, 5507 KiB  
Article
A Social-Network-Based Crowd Selection Approach for Crowdsourcing Mobile Apps Requirements Engineering Tasks
by Ghadah Alamer, Sultan Alyahya and Hmood Al-Dossari
Appl. Sci. 2024, 14(23), 11230; https://doi.org/10.3390/app142311230 - 2 Dec 2024
Viewed by 662
Abstract
Mobile apps have revolutionized almost every aspect of our daily lives, shaping the way we shop, learn and work. The transformative and unprecedented impact they have made on our lifestyle and the convenience they have offered have increased their adoption in diverse domains. [...] Read more.
Mobile apps have revolutionized almost every aspect of our daily lives, shaping the way we shop, learn and work. The transformative and unprecedented impact they have made on our lifestyle and the convenience they have offered have increased their adoption in diverse domains. Therefore, it is of paramount importance to hear from the interested audience about their desires and requirements in mobile apps. This has stressed the need to employ crowdsourcing in requirements engineering (RE) activities to harness the scattered talent in the crowd. RE tasks require certain software domain knowledge, hence, selecting a suitable subset of the crowd is crucial to obtain high-quality contributions. For that, we propose a crowd selection approach for crowdsourcing mobile app requirements engineering tasks which leverages the untapped crowd available on the social network Twitter (recently changed to X). This article is an extension of our previous work, where we present the proposed social-network-based crowd selection approach design, continue to work on the remaining component of the approach and evaluate the approach through a controlled experiment. For evaluation, the approach was utilized to select a real crowd that were invited to contribute to crowdsourcing requirements elicitation tasks for a fitness mobile app. The quality of the crowdsourced requirements was assessed by experts and the results have provided encouraging and compelling insights about the effectiveness of the proposed approach. The obtained assessment scores for the five quality factors clarity, creativity, relatedness, feasibility and diversity were respectively 4.36, 4.01, 4.29, 4.45 and 4.43 out of 5. Overall, we believe that the proposed social-network-based crowd selection approach could help in eliciting mobile app requirements and features that could cater to the needs of a large audience. Full article
(This article belongs to the Special Issue Intelligent Software Engineering: Innovations, Challenges, and Applications)
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