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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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

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Algorithms algorithms	1.8	4.1	2008	18.9 Days	CHF 1600	Submit
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400	Submit
Electronics electronics	2.6	5.3	2012	16.4 Days	CHF 2400	Submit
Machine Learning and Knowledge Extraction make	4.0	6.3	2019	20.8 Days	CHF 1800	Submit
AI ai	3.1	7.2	2020	18.9 Days	CHF 1600	Submit
Software software	-	-	2022	15.7 Days	CHF 1000	Submit

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Published Papers (6 papers)

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All Algorithms Applied Sciences Electronics MAKE AI Software

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23 pages, 602 KiB  

Open AccessArticle

The Scalable Detection and Resolution of Data Clumps Using a Modular Pipeline with ChatGPT

by Nils Baumgartner, Padma Iyenghar, Timo Schoemaker and Elke Pulvermüller

Software 2025, 4(1), 3; https://doi.org/10.3390/software4010003 - 2 Feb 2025

Viewed by 357

Abstract

This paper explores a modular pipeline architecture that integrates ChatGPT, a Large Language Model (LLM), to automate the detection and refactoring of data clumps—a prevalent type of code smell that complicates software maintainability. Data clumps refer to clusters of code that are often [...] Read more.

This paper explores a modular pipeline architecture that integrates ChatGPT, a Large Language Model (LLM), to automate the detection and refactoring of data clumps—a prevalent type of code smell that complicates software maintainability. Data clumps refer to clusters of code that are often repeated and should ideally be refactored to improve code quality. The pipeline leverages ChatGPT’s capabilities to understand context and generate structured outputs, making it suitable for addressing complex software refactoring tasks. Through systematic experimentation, our study not only addresses the research questions outlined but also demonstrates that the pipeline can accurately identify data clumps, particularly excelling in cases that require semantic understanding—where localized clumps are embedded within larger codebases. While the solution significantly enhances the refactoring workflow, facilitating the management of distributed clumps across multiple files, it also presents challenges such as occasional compiler errors and high computational costs. Feedback from developers underscores the usefulness of LLMs in software development but also highlights the essential role of human oversight to correct inaccuracies. These findings demonstrate the pipeline’s potential to enhance software maintainability, offering a scalable and efficient solution for addressing code smells in real-world projects, and contributing to the broader goal of enhancing software maintainability in large-scale projects. Full article

(This article belongs to the Topic Applications of NLP, AI, and ML in Software Engineering)

25 pages, 641 KiB  

Open AccessArticle

A Lexicon-Based Framework for Mining and Analysis of Arabic Comparative Sentences

by Alaa Hamed, Arabi Keshk and Anas Youssef

Algorithms 2025, 18(1), 44; https://doi.org/10.3390/a18010044 - 13 Jan 2025

Viewed by 398

Abstract

People tend to share their opinions on social media daily. This text needs to be accurately mined for different purposes like enhancements in services and/or products. Mining and analyzing Arabic text have been a big challenge due to many complications inherited in Arabic [...] Read more.

People tend to share their opinions on social media daily. This text needs to be accurately mined for different purposes like enhancements in services and/or products. Mining and analyzing Arabic text have been a big challenge due to many complications inherited in Arabic language. Although, many research studies have already investigated the Arabic text sentiment analysis problem, this paper investigates the specific research topic that addresses Arabic comparative opinion mining. This research topic is not widely investigated in many research studies. This paper proposes a lexicon-based framework which includes a set of proposed algorithms for the mining and analysis of Arabic comparative sentences. The proposed framework comprises a set of contributions including an Arabic comparative sentence keywords lexicon and a proposed algorithm for the identification of Arabic comparative sentences, followed by a second proposed algorithm for the classification of identified comparative sentences into different types. The framework also comprises a third proposed algorithm that was developed to extract relations between entities in each of the identified comparative sentence types. Finally, two proposed algorithms were developed for the extraction of the preferred entity in each sentence type. The framework was evaluated using three different Arabic language datasets. The evaluation metrics used to obtain the evaluation results include precision, recall, F-score, and accuracy. The average values of the evaluation metrics for the proposed sentences identification algorithm reached 97%. The average evaluation values of the evaluation metrics for the proposed sentence type identification algorithm reached 96%. Finally, the average results showed 97% relation word extraction precision for the proposed relation extraction algorithm. Full article

(This article belongs to the Topic Applications of NLP, AI, and ML in Software Engineering)

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21 pages, 16988 KiB  

Open AccessArticle

An End-to-End Adaptive Method for Remaining Useful Life Prediction of Rolling Bearings Using Time–Frequency Image Features

by Liang Chen, Hao Wang, Linshu Meng, Zhenzhen Xu, Lin Xue and Mingfa Ren

Mach. Learn. Knowl. Extr. 2024, 6(4), 2892-2912; https://doi.org/10.3390/make6040138 - 16 Dec 2024

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Abstract

The deep learning model has attracted widespread attention in the field of rolling bearing remaining useful life (RUL) prediction due to its advantages of less reliance on prior knowledge, high accuracy, and strong generalization. However, a large number of prediction models use very [...] Read more.

The deep learning model has attracted widespread attention in the field of rolling bearing remaining useful life (RUL) prediction due to its advantages of less reliance on prior knowledge, high accuracy, and strong generalization. However, a large number of prediction models use very complicated artificial feature extraction and selection methods to build the original input features of the deep learning model and health indicator. These approaches do not fully exploit the capabilities of deep learning models as they continue to heavily rely on prior knowledge, The accuracy of their predictions largely hinges on the quality of the input features, and the generalization of manually crafted features remains uncertain. To address these challenges, in this paper, an end-to-end prediction model for the remaining useful life of rolling bearings is proposed, which is divided into three modules. First, a short-term Fourier transform module is incorporated into the model to automatically obtain the time–frequency information of the signal. Then, the convolutional next (ConvNext) module, which is a simple and efficient pure convolutional neural network, is utilized to extract features from the spectrogram. Finally, we capture the short-term dependence and long-term dependence by two parallel channels Transformer and self-attention convolutional long short-term memory (SA-ConvLSTM), and the self-attention mechanism is employed for the adaptive prediction of the bearing’s remaining useful life. Through integration with artificial intelligence, this method proposes a high-performance solution for predicting the remaining useful life of bearings. It has minimal reliance on manual labor, stronger fitting capabilities, and can be widely used for predicting the remaining useful life of bearings. Full article

(This article belongs to the Topic Applications of NLP, AI, and ML in Software Engineering)

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12 pages, 293 KiB  

Open AccessArticle

Detecting Online Sexism: Integrating Sentiment Analysis with Contextual Language Models

by Faiza Belbachir, Thomas Roustan and Assia Soukane

AI 2024, 5(4), 2852-2863; https://doi.org/10.3390/ai5040137 - 10 Dec 2024

Viewed by 1061

Abstract

In the digital era, social media platforms have seen a substantial increase in the volume of online comments. While these platforms provide users with a space to express their opinions, they also serve as fertile ground for the proliferation of hate speech. Hate [...] Read more.

In the digital era, social media platforms have seen a substantial increase in the volume of online comments. While these platforms provide users with a space to express their opinions, they also serve as fertile ground for the proliferation of hate speech. Hate comments can be categorized into various types, including discrimination, violence, racism, and sexism, all of which can negatively impact mental health. Among these, sexism poses a significant challenge due to its various forms and the difficulty in defining it, making detection complex. Nevertheless, detecting and preventing sexism on social networks remains a critical issue. Recent studies have leveraged language models such as transformers, known for their ability to capture the semantic nuances of textual data. In this study, we explore different transformer models, including multiple versions of RoBERTa (A Robustly Optimized BERT Pretraining Approach), to detect sexism. We hypothesize that combining a sentiment-focused language model with models specialized in sexism detection can improve overall performance. To test this hypothesis, we developed two approaches. The first involved using classical transformers trained on our dataset, while the second combined embeddings generated by transformers with a Long Short-Term Memory (LSTM) model for classification. The probabilistic outputs of each approach were aggregated through various voting strategies to enhance detection accuracy. The LSTM with embeddings approach improved the F1-score by 0.2% compared to the classical transformer approach. Furthermore, the combination of both approaches confirms our hypothesis, achieving a 1.6% improvement in the F1-score in each case. We determined that an F1 score of over 0.84 effectively measures sexism. Additionally, we constructed our own dataset to train and evaluate the models. Full article

(This article belongs to the Topic Applications of NLP, AI, and ML in Software Engineering)

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22 pages, 1085 KiB  

Open AccessArticle

SevPredict: Exploring the Potential of Large Language Models in Software Maintenance

by Muhammad Ali Arshad, Adnan Riaz, Rubia Fatima and Affan Yasin

AI 2024, 5(4), 2739-2760; https://doi.org/10.3390/ai5040132 - 5 Dec 2024

Viewed by 938

Abstract

The prioritization of bug reports based on severity is a crucial aspect of bug triaging, enabling a focus on more critical issues. Traditional methods for assessing bug severity range from manual inspection to the application of machine and deep learning techniques. However, manual [...] Read more.

The prioritization of bug reports based on severity is a crucial aspect of bug triaging, enabling a focus on more critical issues. Traditional methods for assessing bug severity range from manual inspection to the application of machine and deep learning techniques. However, manual evaluation tends to be resource-intensive and inefficient, while conventional learning models often lack contextual understanding. This study explores the effectiveness of large language models (LLMs) in predicting bug report severity. We propose a novel approach called SevPredict using GPT-2, an advanced LLM, and compare it against state-of-the-art models. The comparative analysis between the proposed approach and state-of-the-art approaches suggests that the proposed approach outperforms the state-of-the-art approaches in terms of performance evaluation metrics. SevPredict shows improvements over the best-performing state-of-the-art approach (BERT-SBR) with 1.72% higher accuracy, 2.18% higher precision, and 4.94% higher MCC. The improvements are even more substantial when compared to the approach by Ramay et al., with SevPredict demonstrating 10.66% higher accuracy, 10.39% higher precision, 3.29% higher recall, 7.19% higher F1-score, and a remarkable 41.27% higher MCC. These findings not only demonstrate the superiority of our GPT-2-based approach in predicting the severity of bug reports but also highlight its potential to significantly advance automated bug triaging and software maintenance. This research introduces a severity prediction tool named SevPredict. Full article

(This article belongs to the Topic Applications of NLP, AI, and ML in Software Engineering)

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19 pages, 2078 KiB  

Open AccessArticle

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 1208

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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