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Electronics
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Intelligent Approaches for Solving Software Problems with AI Techniques
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Intelligent Approaches for Solving Software Problems with AI Techniques

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

Deadline for manuscript submissions: 15 February 2025 | Viewed by 8940

Special Issue Editors

Dr. Yeong-Seok Seo

E-Mail Website
Guest Editor
Department of Computer Engineering, Yeungnam University, Gyeongsan 38541, Gyeongbuk, Republic of Korea
Interests: data analysis; artificial intelligence; software engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Jun-Ho Huh

E-Mail Website
Guest Editor
Department of Data Science, National Korea Maritime and Ocean University, Busan 49112, Republic of Korea
Interests: AI; big data; virtualization; shipbuilding industry; shipping industry; Industry 4.0
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) technology, especially multimodal AI and Large Language Models (LLMs), has been rapidly advancing in recent years. This progress has brought innovative changes to traditional software development and methods of solving software problems. These AI technologies can understand and process various forms of data, integrating these forms of data to produce new analyses and outcomes. Therefore, we aim to discuss intelligent approaches to solve existing software problems using these rapidly evolving AI technologies.

While these latest AI technologies are improving our lives in various ways, they come with several limitations and challenges. For instance, the integration and optimization of multimodal AI systems require significant technical complexity, and LLMs sometimes produce uncertain information. Additionally, models based on the latest AI technologies demand substantial data and training costs for software operation. Thus, continuous research and development are essential to effectively utilize these latest AI technologies.

We will explore how these latest AI technologies can enhance the quality, efficiency, and security of software. For example, we aim to study and share how multimodal AI can offer intelligent methods for software development by integrating various data types, or how LLMs can optimize complex linguistic processing tasks and contribute to software development. Besides multimodal AI and LLM, there are various other latest AI technologies, and we plan to share specific case studies and research results on how these technologies contribute to software development.

This Special Issue welcomes original research papers and reviews. The research areas can include, but are not limited to:

  • Methodologies for solving software problems using AI technologies;
  • Effective integration/application methods of AI technologies with existing approaches;
  • AI solutions in various industries;
  • Software testing and quality control methods using AI;
  • Methodologies for privacy protection and data security;
  • Effective interaction methods between humans and AI;
  • Data collection and preprocessing methods based on the latest AI technology;
  • Software development and limitations using multimodality or cross-modality;
  • Evaluation methods for successful AI service development;
  • Ethical and legal aspects of software developed using AI technology;
  • Model lightening and optimization of super-large AI;
  • Service cases using super-large AI;
  • LLM (e.g., GPT, BERT, LLaMA, Alpaca) applications for solving existing problems.

Through this Special Issue, we hope that your research and experience will deepen our understanding in this field and contribute to the application and advancement of the latest AI technologies in software development.

Dr. Yeong-Seok Seo
Dr. Jun-Ho Huh
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. 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

  • artificial intelligence
  • software engineering
  • AI service development
  • multi-modal
  • large language model (e.g., GPT, BERT, LlaMa, Alpaca)
  • software development
  • model lightening
  • model optimization
  • data security
  • data integration

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Published Papers (4 papers)

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Research

23 pages, 1147 KiB  
Article
Mutation-Based Approach to Supporting Human–Machine Pair Inspection
by Yujun Dai, Shaoying Liu and Haiyi Liu
Electronics 2025, 14(2), 382; https://doi.org/10.3390/electronics14020382 - 19 Jan 2025
Viewed by 329
Abstract
Human–machine pair inspection refers to a technique that supports programmers and machines working together as a “pair” in source code inspection tasks. The machine provides guidance, while the programmer performs the inspection based on this guidance. Although programmers are often best suited to [...] Read more.
Human–machine pair inspection refers to a technique that supports programmers and machines working together as a “pair” in source code inspection tasks. The machine provides guidance, while the programmer performs the inspection based on this guidance. Although programmers are often best suited to inspect their own code due to familiarity, overconfidence may lead them to overlook important details. This study introduces a novel mutation-based human–machine pair inspection method, which is designed to direct the programmer’s attention to specific code components by applying targeted mutations. We assess the effectiveness of code inspections by analyzing the programmer’s corrections of these mutations. Our approach involves defining mutation operators for each keyword in the program based on historical defects, developing mutation rules based on program keywords and a strategy for automatically generating mutants, and designing a code comparison strategy to quantitatively evaluate code inspection quality. Through a controlled experiment, we demonstrate the effectiveness of mutation-based human–machine pair inspection in aiding programmers during the inspection process. Full article
(This article belongs to the Special Issue Intelligent Approaches for Solving Software Problems with AI Techniques)
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23 pages, 4893 KiB  
Article
Enhancing Software Effort Estimation with Pre-Trained Word Embeddings: A Small-Dataset Solution for Accurate Story Point Prediction
by Issa Atoum and Ahmed Ali Otoom
Electronics 2024, 13(23), 4843; https://doi.org/10.3390/electronics13234843 - 8 Dec 2024
Viewed by 1020
Abstract
Traditional software effort estimation methods, such as term frequency–inverse document frequency (TF-IDF), are widely used due to their simplicity and interpretability. However, they struggle with limited datasets, fail to capture intricate semantics, and suffer from dimensionality, sparsity, and computational inefficiency. This study used [...] Read more.
Traditional software effort estimation methods, such as term frequency–inverse document frequency (TF-IDF), are widely used due to their simplicity and interpretability. However, they struggle with limited datasets, fail to capture intricate semantics, and suffer from dimensionality, sparsity, and computational inefficiency. This study used pre-trained word embeddings, including FastText and GPT-2, to improve estimation accuracy in such cases. Seven pre-trained models were evaluated for their ability to effectively represent textual data, addressing the fundamental limitations of TF-IDF through contextualized embeddings. The results show that combining FastText embeddings with support vector machines (SVMs) consistently outperforms traditional approaches, reducing the mean absolute error (MAE) by 5–18% while achieving accuracy comparable to deep learning models like GPT-2. This approach demonstrated the adaptability of pre-trained embeddings for small datasets, balancing semantic richness with computational efficiency. The proposed method optimized project planning and resource allocation while enhancing software development through accurate story point prediction while safeguarding privacy and security through data anonymization. Future research will explore task-specific embeddings tailored to software engineering domains and investigate how dataset characteristics, such as cultural variations, influence model performance, ensuring the development of adaptable, robust, and secure machine learning models for diverse contexts. Full article
(This article belongs to the Special Issue Intelligent Approaches for Solving Software Problems with AI Techniques)
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20 pages, 2165 KiB  
Article
Software Weakness Detection in Solidity Smart Contracts Using Control and Data Flow Analysis: A Novel Approach with Graph Neural Networks
by Aria Seo, Young-Tak Kim, Ji Seok Yang, YangSun Lee and Yunsik Son
Electronics 2024, 13(16), 3162; https://doi.org/10.3390/electronics13163162 - 10 Aug 2024
Viewed by 1569
Abstract
Smart contracts on blockchain platforms are susceptible to security issues that can lead to significant financial losses. This study converts the Solidity code into abstract syntax trees and generates control flow graphs and data flow graphs. These graphs train a graph convolutional network [...] Read more.
Smart contracts on blockchain platforms are susceptible to security issues that can lead to significant financial losses. This study converts the Solidity code into abstract syntax trees and generates control flow graphs and data flow graphs. These graphs train a graph convolutional network model to detect security weaknesses. The proposed system outperforms traditional tools, achieving higher accuracy, recall, precision, and F1 scores when detecting weaknesses such as integer overflow/underflow, reentrancy, delegate call to the untrusted callee, and time-based issues. This study demonstrates that leveraging control and data flow analysis with graph neural networks significantly enhances smart contract security and provides a robust and reliable solution. Full article
(This article belongs to the Special Issue Intelligent Approaches for Solving Software Problems with AI Techniques)
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26 pages, 560 KiB  
Article
Prompt Engineering in Healthcare
by Rajvardhan Patil, Thomas F. Heston and Vijay Bhuse
Electronics 2024, 13(15), 2961; https://doi.org/10.3390/electronics13152961 - 26 Jul 2024
Cited by 5 | Viewed by 5419
Abstract
The rapid advancements in artificial intelligence, particularly generative AI and large language models, have unlocked new possibilities for revolutionizing healthcare delivery. However, harnessing the full potential of these technologies requires effective prompt engineering—designing and optimizing input prompts to guide AI systems toward generating [...] Read more.
The rapid advancements in artificial intelligence, particularly generative AI and large language models, have unlocked new possibilities for revolutionizing healthcare delivery. However, harnessing the full potential of these technologies requires effective prompt engineering—designing and optimizing input prompts to guide AI systems toward generating clinically relevant and accurate outputs. Despite the importance of prompt engineering, medical education has yet to fully incorporate comprehensive training on this critical skill, leading to a knowledge gap among medical clinicians. This article addresses this educational gap by providing an overview of generative AI prompt engineering, its potential applications in primary care medicine, and best practices for its effective implementation. The role of well-crafted prompts in eliciting accurate, relevant, and valuable responses from AI models is discussed, emphasizing the need for prompts grounded in medical knowledge and aligned with evidence-based guidelines. The article explores various applications of prompt engineering in primary care, including enhancing patient–provider communication, streamlining clinical documentation, supporting medical education, and facilitating personalized care and shared decision-making. Incorporating domain-specific knowledge, engaging in iterative refinement and validation of prompts, and addressing ethical considerations and potential biases are highlighted. Embracing prompt engineering as a core competency in medical education will be crucial for successfully adopting and implementing AI technologies in primary care, ultimately leading to improved patient outcomes and enhanced healthcare delivery. Full article
(This article belongs to the Special Issue Intelligent Approaches for Solving Software Problems with AI Techniques)
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