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Applications of NLP, AI, and ML in Software Engineering
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School of Software, Northwestern Polytechnical University (NPU), Xi'an, China
Foundation of Software Engineering (FSE) Group, Department of Software Engineering, Faculty of Physics, Engineering, and Computer Science, University of Hertfordshire, Hatfield, UK
Dr. Lijie Wen
School of Software, Tsinghua University, Beijing, China
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Applications of NLP, AI, and ML in Software Engineering

Abstract submission deadline
30 June 2025
Manuscript submission deadline
31 August 2025
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1813

Topic Information

Dear Colleagues,

The integration of Natural Language Processing (NLP), Artificial Intelligence (AI), and Machine Learning (ML) into Software Engineering is revolutionizing the way software is developed, tested, and maintained. These advanced technologies enable the automation of complex tasks, improve accuracy in bug detection, and enhance code quality. By leveraging NLP, AI, and ML, software engineers can better manage requirements, optimize project workflows, and predict project risks. This topic seeks to showcase cutting-edge research and practical applications that demonstrate the transformative potential of these technologies in the software engineering domain. We invite contributions that explore innovative methodologies, practical tools, and real-world case studies. High-quality studies comparing the efficiency of various algorithms on different datasets are also of particular interest. Such comparative analyses are crucial for understanding the strengths and weaknesses of different approaches, thereby guiding practitioners in selecting the most appropriate techniques for their specific needs. These studies provide valuable insights into algorithm performance, scalability, and adaptability across diverse software engineering contexts. One compelling example of the application of NLP, AI, and ML in Software Engineering is the automated generation of code documentation. By utilizing NLP techniques, AI models can analyze the codebase and generate comprehensive documentation that explains the functionality of the code in human-readable language. This not only saves significant time for developers but also ensures that the documentation is always up-to-date with the latest code changes. Additionally, ML algorithms can be used to predict potential areas in the code that are prone to bugs or require refactoring, further enhancing the efficiency and reliability of the software development process.

Dr. Affan Yasin
Dr. Javed Ali Khan
Dr. Lijie Wen
Topic Editors

Keywords

  • natural language processing (NLP)
  • artificial intelligence (AI)
  • machine learning (ML)
  • software engineering
  • algorithm comparison
  • requirements engineering
  • bug detection
  • performance analysis
  • code quality
  • predictive analytics

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Algorithms
algorithms 		1.8 4.1 2008 15 Days CHF 1600 Submit
Applied Sciences
applsci 		2.5 5.3 2011 17.8 Days CHF 2400 Submit
Electronics
electronics 		2.6 5.3 2012 16.8 Days CHF 2400 Submit
Machine Learning and Knowledge Extraction
make 		4.0 6.3 2019 27.1 Days CHF 1800 Submit
AI
ai 		3.1 7.2 2020 17.6 Days CHF 1600 Submit
Software
software 		- - 2022 19.8 Days CHF 1000 Submit

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Published Papers (1 paper)

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19 pages, 2078 KiB  
Article
Enhancing Medical Image Classification with Unified Model Agnostic Computation and Explainable AI
by Elie Neghawi and Yan Liu
AI 2024, 5(4), 2260-2278; https://doi.org/10.3390/ai5040111 - 5 Nov 2024
Viewed by 688
Abstract
Background: Advances in medical image classification have recently benefited from general augmentation techniques. However, these methods often fall short in performance and interpretability. Objective: This paper applies the Unified Model Agnostic Computation (UMAC) framework specifically to the medical domain to demonstrate [...] Read more.
Background: Advances in medical image classification have recently benefited from general augmentation techniques. However, these methods often fall short in performance and interpretability. Objective: This paper applies the Unified Model Agnostic Computation (UMAC) framework specifically to the medical domain to demonstrate its utility in this critical area. Methods: UMAC is a model-agnostic methodology designed to develop machine learning approaches that integrate seamlessly with various paradigms, including self-supervised, semi-supervised, and supervised learning. By unifying and standardizing computational models and algorithms, UMAC ensures adaptability across different data types and computational environments while incorporating state-of-the-art methodologies. In this study, we integrate UMAC as a plug-and-play module within convolutional neural networks (CNNs) and Transformer architectures, enabling the generation of high-quality representations even with minimal data. Results: Our experiments across nine diverse 2D medical image datasets show that UMAC consistently outperforms traditional data augmentation methods, achieving a 1.89% improvement in classification accuracy. Conclusions: Additionally, by incorporating explainable AI (XAI) techniques, we enhance model transparency and reliability in decision-making. This study highlights UMAC’s potential as a powerful tool for improving both the performance and interpretability of medical image classification models. Full article
(This article belongs to the Topic Applications of NLP, AI, and ML in Software Engineering)
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