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Applied Sciences
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Artificial Intelligence in Software Engineering
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Artificial Intelligence in Software Engineering

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

Deadline for manuscript submissions: 30 September 2024 | Viewed by 593

Special Issue Editors

Prof. Dr. Linda Vickovic

E-Mail Website
Guest Editor
Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, 21000 Split, Croatia
Interests: software engineering; complex data storage systems
Dr. Maja Braović

E-Mail Website
Guest Editor
Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, 21000 Split, Croatia
Interests: artificial intelligence; computer vision; machine learning; natural language processing; computational linguistics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are inviting submissions to this Special Issue on Artificial Intelligence in Software Engineering.

In recent years, artificial intelligence (AI) has emerged as a disruptive technology with the potential to revolutionize various industries, and software engineering (SE) is no exception. The significant impact of AI paradigms (such as neural networks, machine learning, knowledge-based systems, and natural language processing) on SE phases (requirements, design, development, testing, release, and maintenance) could be used to improve the process and eliminate many of the major challenges that the SE field has been facing. Some of the areas where AI can assist SE processes are AI-powered requirement analysis and planning, enhanced code generation and automation, AI-driven bug detection and debugging, smart testing and quality assurance, personalization and user experience optimization, Natural Language Processing (NLP) and voice interfaces, predictive analytics and decision making, AI for Continuous Integration and Continuous Deployment (CI/CD), and autonomous software maintenance.

In this Special Issue, we invite submissions that explore cutting-edge research and recent advances in the fields of artificial intelligence in software engineering. Both theoretical and experimental studies are welcome, as well as comprehensive review and survey papers.

Prof. Dr. Linda Vickovic
Dr. Maja Braović
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
  • artificial intelligence
  • AI in requirement analysis
  • smart testing and quality assurance
  • predictive analytics and decision making
  • AI for continuous integration and continuous deployment (CI/CD)
  • autonomous software maintenance

Published Papers (1 paper)

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37 pages, 4751 KiB  
Systematic Review
Machine Learning-Based Methods for Code Smell Detection: A Survey
by Pravin Singh Yadav, Rajwant Singh Rao, Alok Mishra and Manjari Gupta
Appl. Sci. 2024, 14(14), 6149; https://doi.org/10.3390/app14146149 - 15 Jul 2024
Viewed by 244
Abstract
Code smells are early warning signs of potential issues in software quality. Various techniques are used in code smell detection, including the Bayesian approach, rule-based automatic antipattern detection, antipattern identification utilizing B-splines, Support Vector Machine direct, SMURF (Support Vector Machines for design smell [...] Read more.
Code smells are early warning signs of potential issues in software quality. Various techniques are used in code smell detection, including the Bayesian approach, rule-based automatic antipattern detection, antipattern identification utilizing B-splines, Support Vector Machine direct, SMURF (Support Vector Machines for design smell detection using relevant feedback), and immune-based detection strategy. Machine learning (ML) has taken a great stride in this area. This study includes relevant studies applying ML algorithms from 2005 to 2024 in a comprehensive manner for the survey to provide insight regarding code smell, ML algorithms frequently applied, and software metrics. Forty-two pertinent studies allow us to assess the efficacy of ML algorithms on selected datasets. After evaluating various studies based on open-source and project datasets, this study evaluated additional threats and obstacles to code smell detection, such as the lack of standardized code smell definitions, the difficulty of feature selection, and the challenges of handling large-scale datasets. The current studies only considered a few factors in identifying code smells, while in this study, several potential contributing factors to code smells are included. Several ML algorithms are examined, and various approaches, datasets, dataset languages, and software metrics are presented. This study provides the potential of ML algorithms to produce better results and fills a gap in the body of knowledge by providing class-wise distributions of the ML algorithms. Support Vector Machine, J48, Naive Bayes, and Random Forest models are the most common for detecting code smells. Researchers can find this study helpful in better anticipating and taking care of software development design and implementation issues. The findings from this study, which highlight the practical implications of ML algorithms in software quality improvement, will help software engineers fix problems during software design and development to ensure software quality. Full article
(This article belongs to the Special Issue Artificial Intelligence in Software Engineering)
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