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Applied Sciences
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AI in Software Engineering: Challenges, Solutions and Applications
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AI in Software Engineering: Challenges, Solutions 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: 20 June 2025 | Viewed by 13294

Special Issue Editors

Prof. Dr. Zhenyu Chen

E-Mail Website
Guest Editor
Software Institute, Nanjing University, Nanjing 210093, China
Interests: collective intelligence; deep learning testing and optimization; big data quality; mobile application testing
Special Issues, Collections and Topics in MDPI journals
Dr. Chunrong Fang

E-Mail Website
Guest Editor
Software Institute, Nanjing University, Nanjing 210093, China
Interests: crowdsourced testing; mobile application testing; deep learning testing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Researchers and practitioners are invited to submit original studies for the Special Issue on “AI in Software Engineering: Challenges, Solutions and Applications”, which aims to explore the transformative role of artificial intelligence in software engineering, addressing both current challenges and pioneering solutions. We welcome submissions covering AI-driven approaches to software engineering, design, testing, maintenance, and project management. Topics may also include innovative AI applications for code generation, debugging, quality assurance, and enhancing developer productivity. This Special Issue offers a platform for multidisciplinary insights that bridge AI advancements and software engineering needs. Join us in advancing knowledge on AI’s potential to shape the future of software engineering.

We look forward to receiving your contributions.

Prof. Dr. Zhenyu Chen
Dr. Chunrong Fang
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 engineering
  • software testing
  • software quality assurance
  • artificial intelligence
  • collective intelligence
  • application testing
  • crowdsourced testing
  • deep learning testing

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Published Papers (4 papers)

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Research

24 pages, 2037 KiB  
Article
Enhancing Software Quality with AI: A Transformer-Based Approach for Code Smell Detection
by Israr Ali, Syed Sajjad Hussain Rizvi and Syed Hasan Adil
Appl. Sci. 2025, 15(8), 4559; https://doi.org/10.3390/app15084559 - 21 Apr 2025
Viewed by 192
Abstract
Software quality assurance is a critical aspect of software engineering, directly impacting maintainability, extensibility, and overall system performance. Traditional machine-learning techniques, such as gradient boosting and support vector machines (SVM), have demonstrated effectiveness in code smell detection but require extensive feature engineering and [...] Read more.
Software quality assurance is a critical aspect of software engineering, directly impacting maintainability, extensibility, and overall system performance. Traditional machine-learning techniques, such as gradient boosting and support vector machines (SVM), have demonstrated effectiveness in code smell detection but require extensive feature engineering and struggle to capture intricate semantic dependencies in software structures. In this study, we introduce Relation-Aware BERT (RABERT), a novel transformer-based model that integrates relational embeddings to enhance automated code smell detection. By modeling interdependencies among software complexity metrics, RABERT surpasses classical machine-learning methods, achieving an accuracy of 90.0% and a precision of 91.0%. However, challenges such as low recall (53.0%) and computational overhead indicate the need for further optimization. We present a comprehensive comparative analysis between classical machine-learning models and transformer-based architectures, evaluating their computational efficiency and predictive capabilities. Our findings contribute to the advancement of AI-driven software quality assurance, offering insights into optimizing transformer-based models for practical deployment in software development workflows. Future research will focus on lightweight transformer variants, cost-sensitive learning techniques, and cross-language generalizability to enhance real-world applicability. Full article
(This article belongs to the Special Issue AI in Software Engineering: Challenges, Solutions and Applications)
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31 pages, 7097 KiB  
Article
Large Language Model Based Intelligent Fault Information Retrieval System for New Energy Vehicles
by Haiyu Zhang, Yinghui Zhao, Boyu Sun, Yaqi Wu, Zetian Fu and Xinqing Xiao
Appl. Sci. 2025, 15(7), 4034; https://doi.org/10.3390/app15074034 - 6 Apr 2025
Viewed by 430
Abstract
In recent years, the rapid development of the new energy vehicle (NEV) industry has exposed significant deficiencies in intelligent fault diagnosis and information retrieval technologies, especially in intelligent fault information retrieval, which faces persistent challenges including inadequate system adaptability and reasoning bottlenecks. To [...] Read more.
In recent years, the rapid development of the new energy vehicle (NEV) industry has exposed significant deficiencies in intelligent fault diagnosis and information retrieval technologies, especially in intelligent fault information retrieval, which faces persistent challenges including inadequate system adaptability and reasoning bottlenecks. To address these challenges, this study proposes a Retrieval-Augmented Generation (RAG) framework that integrates large language models (LLMs) with knowledge graphs (KGs). The framework consists of three key components: fault data collection, knowledge graph construction, and fault knowledge model training. The primary research contributions are threefold: (1) A domain-optimized fine-tuning strategy for LLMs based on NEV fault characteristics, verifying the superior accuracy of the Bidirectional Encoder Representations from Transformers (BERT) model in fault classification tasks. (2) A structured knowledge graph encompassing 122 fault categories, developed through the ChatGLM3-6B model completing named entity and knowledge relation extraction to generate fault knowledge and build a paraphrased vocabulary. (3) An intelligent fault information retrieval system that significantly outperforms traditional models in NEV-specific Q&A scenarios, providing multi-level fault cause analysis and actionable solution recommendations. Full article
(This article belongs to the Special Issue AI in Software Engineering: Challenges, Solutions and Applications)
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26 pages, 522 KiB  
Article
AI-Driven Innovations in Software Engineering: A Review of Current Practices and Future Directions
by Mamdouh Alenezi and Mohammed Akour
Appl. Sci. 2025, 15(3), 1344; https://doi.org/10.3390/app15031344 - 28 Jan 2025
Cited by 3 | Viewed by 12683
Abstract
The software engineering landscape is undergoing a significant transformation with the advent of artificial intelligence (AI). AI technologies are poised to redefine traditional software development practices, offering innovative solutions to long-standing challenges. This paper explores the integration of AI into software engineering processes, [...] Read more.
The software engineering landscape is undergoing a significant transformation with the advent of artificial intelligence (AI). AI technologies are poised to redefine traditional software development practices, offering innovative solutions to long-standing challenges. This paper explores the integration of AI into software engineering processes, aiming to identify its impacts, benefits, and the challenges that accompany this paradigm shift. A comprehensive analysis of current AI applications in software engineering is conducted, supported by case studies and theoretical models. The study examines various phases of software development to assess where AI contributes most effectively. The integration of AI enhances productivity, improves code quality, and accelerates development cycles. Key areas of impact include automated code generation, intelligent debugging, predictive maintenance, and enhanced decision-making processes. AI is revolutionizing software engineering by introducing automation and intelligence into the development lifecycle. Embracing AI-driven tools and methodologies is essential for staying competitive in the evolving technological landscape. Full article
(This article belongs to the Special Issue AI in Software Engineering: Challenges, Solutions and Applications)
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29 pages, 1332 KiB  
Article
Enhancing Conversational Agent Development Through a Semi-Automatization Development Proposal
by Ángel Martínez-Gárate, José Alfonso Aguilar-Calderón, Carolina Tripp-Barba and Aníbal Zaldívar-Colado
Appl. Sci. 2025, 15(3), 1139; https://doi.org/10.3390/app15031139 - 23 Jan 2025
Viewed by 808
Abstract
The development of chatbots is often hindered by high costs and time consumption, with existing technologies and frameworks not fully addressing the diverse requirements for creating robust conversational agents across platforms like WhatsApp. This is a limitation for the implementation of these frameworks [...] Read more.
The development of chatbots is often hindered by high costs and time consumption, with existing technologies and frameworks not fully addressing the diverse requirements for creating robust conversational agents across platforms like WhatsApp. This is a limitation for the implementation of these frameworks in a professional environment, thus representing both opportunities and complexities. This article presents improvements to the Xatkit framework, utilizing Model-Driven Development techniques to simplify chatbot development and extend support for WhatsApp. Additionally, the integration of the GPT-3 autoregressive language model enhances user interaction, offering a more sophisticated and responsive conversational experience. Full article
(This article belongs to the Special Issue AI in Software Engineering: Challenges, Solutions and Applications)
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