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Mathematics
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Advances in Mathematics Computation for Software Engineering
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Advances in Mathematics Computation for Software Engineering

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 2882

Special Issue Editors

Dr. Ziaur Rahman

E-Mail Website
Guest Editor
Faculty of Science, School of Computer Science, Queensland University of Technology (QUT), Brisbane, QLD 4001, Australia
Interests: privacy-preserved IoT security; post-quantum cryptography; blockchain technology; edge machine learning; software security
Dr. Dipankar Chaki

E-Mail Website
Guest Editor
School of Computer Science & Engineering, University of New South Wales (UNSW), Sydney, NSW 2052, Australia
Interests: software security; machine learning; deep learning; Internet of Things (IoT); service computing
Dr. Chao Lin

E-Mail Website
Guest Editor
College of Computer and Cyber Security, Fujian Normal University, Qishan Campus, No. 1 Keji Road, Shangjie, Minhou 350117, China
Interests: cryptography privacy in blockchain; federated learning

Special Issue Information

Dear Colleagues,

This Special Issue is dedicated to publishing original research papers that explore the intersection of mathematics and software engineering, particularly in the realm of computational advancements. It emphasizes the development of innovative theories and methods that contribute to this dynamic field. The scope of this Special Issue encompasses the design, implementation, and analysis of sophisticated algorithms and software tools. These tools are pivotal for mathematical computation and reasoning, especially in their application to scientific and engineering challenges.

A key area of focus is the convergence of mathematical computation with software engineering. This convergence, though highly abstract, is instrumental in advancing computer science. At its core, this Special Issue explores software tools and algorithms grounded in mathematical models, underscoring the foundational role of mathematics in computer science. It delves into applied mathematics, particularly discrete mathematics, which plays a crucial role in converting continuous models into discrete forms, thereby facilitating the interaction between computers and human understanding.

Furthermore, this Special Issue highlights the growing importance of mathematical proofs in computer science. These proofs are essential for certifying the reliability of software and hardware systems, offering assurances beyond what testing alone can provide.

Overall, this Special Issue seeks to capture the latest developments and research in mathematical computation and software engineering, covering a wide range of methods, approaches, and related topics.

Topics of this Special Issue include (but not limited to):

  • Algorithmic game theory and mechanism design in software engineering;
  • Mathematical modelling of software engineering metrics;
  • Fuzzy logic and fuzzy systems;
  • Fuzzy cognitive maps, fuzzy decision making, and fuzzy optimization;
  • Artificial neural networks;
  • Bayesian belief networks;
  • Probabilistic reasoning;
  • Support vector machines;
  • Evolutionary algorithms and evolutionary computation;
  • Differential evolution;
  • Intelligent software agent systems and architectures;
  • Chaos theory;
  • Software release planning;
  • Requirements engineering;
  • Requirements prioritization;
  • Software size and cost estimation;
  • Software testing;
  • Software defect prediction;
  • Bug triaging;
  • Software project and process management;
  • Software project risk management;
  • Software architectural decisions;
  • Software architecture design, evaluation, and recovery;
  • Prediction of reusability, maintainability, and testability;
  • Prediction of quality and vulnerability;
  • Cloud application, infrastructure, and platforms;
  • Design tool for cloud computing;
  • Cloud business;
  • Service-oriented architecture in cloud computing;
  • Cloud-based parallel processing;
  • Virtualization on platforms in the cloud;
  • Location-based services, presence, availability, and locality;
  • Mobile clouds for new-millennium, mobile devices;
  • Social clouds (social networks in the cloud);
  • Maintenance and management of cloud computing.

Dr. Ziaur Rahman
Dr. Dipankar Chaki
Dr. Chao Lin
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. Mathematics 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 2600 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

  • optimization
  • algorithms for application domains
  • software engineering
  • machine learning algorithms

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

14 pages, 433 KiB  
Article
Attribute-Based Designated Combiner Transitive Signature Scheme
by Shaonan Hou, Shaojun Yang and Chengjun Lin
Mathematics 2024, 12(19), 3070; https://doi.org/10.3390/math12193070 - 30 Sep 2024
Viewed by 340
Abstract
Transitive signatures allow any entity to obtain a valid signature of (i,k) by combining signatures of (i,j) and (j,k). However, the traditional transitive signature scheme does not offer fine-grained control [...] Read more.
Transitive signatures allow any entity to obtain a valid signature of (i,k) by combining signatures of (i,j) and (j,k). However, the traditional transitive signature scheme does not offer fine-grained control over the combiner. To address this issue, we propose a formal definition of the attribute-based designated combiner transitive signature (ABDCTS) and its security model, where only entities whose inherent attributes meet the access policy can combine signatures. By introducing the fine-grained access control structure, control over the combiner is achieved. To demonstrate the feasibility of our primitive, this paper presents the first attribute-based designated combiner transitive signature scheme. Under an adaptive chosen-message attack, we prove its security based on the one-more CDH problem and the co-CDH problem, and that its algorithms have robustness. Full article
(This article belongs to the Special Issue Advances in Mathematics Computation for Software Engineering)
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28 pages, 24699 KiB  
Article
Enhancing Autism Spectrum Disorder Classification with Lightweight Quantized CNNs and Federated Learning on ABIDE-1 Dataset
by Simran Gupta, Md. Rahad Islam Bhuiyan, Sadia Sultana Chowa, Sidratul Montaha, Rashik Rahman, Sk. Tanzir Mehedi and Ziaur Rahman
Mathematics 2024, 12(18), 2886; https://doi.org/10.3390/math12182886 - 16 Sep 2024
Viewed by 982
Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental condition that presents significant diagnostic challenges due to its varied symptoms and nature. This study aims to improve ASD classification using advanced deep learning techniques applied to neuroimaging data. We developed an automated system leveraging [...] Read more.
Autism spectrum disorder (ASD) is a complex neurodevelopmental condition that presents significant diagnostic challenges due to its varied symptoms and nature. This study aims to improve ASD classification using advanced deep learning techniques applied to neuroimaging data. We developed an automated system leveraging the ABIDE-1 dataset and a novel lightweight quantized one-dimensional (1D) Convolutional Neural Network (Q-CNN) model to analyze fMRI data. Our approach employs the NIAK pipeline with multiple brain atlases and filtering methods. Initially, the Regions of Interest (ROIs) are converted into feature vectors using tangent space embedding to feed into the Q-CNN model. The proposed 1D-CNN is quantized through Quantize Aware Training (QAT). As the quantization method, int8 quantization is utilized, which makes it both robust and lightweight. We propose a federated learning (FL) framework to ensure data privacy, which allows decentralized training across different data centers without compromising local data security. Our findings indicate that the CC200 brain atlas, within the NIAK pipeline’s filt-global filtering methods, provides the best results for ASD classification. Notably, the ASD classification outcomes have achieved a significant test accuracy of 98% using the CC200 and filt-global filtering techniques. To the best of our knowledge, this performance surpasses previous studies in the field, highlighting a notable enhancement in ASD detection from fMRI data. Furthermore, the FL-based Q-CNN model demonstrated robust performance and high efficiency on a Raspberry Pi 4, underscoring its potential for real-world applications. We exhibit the efficacy of the Q-CNN model by comparing its inference time, power consumption, and storage requirements with those of the 1D-CNN, quantized CNN, and the proposed int8 Q-CNN models. This research has made several key contributions, including the development of a lightweight int8 Q-CNN model, the application of FL for data privacy, and the evaluation of the proposed model in real-world settings. By identifying optimal brain atlases and filtering methods, this study provides valuable insights for future research in the field of neurodevelopmental disorders. Full article
(This article belongs to the Special Issue Advances in Mathematics Computation for Software Engineering)
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22 pages, 987 KiB  
Article
Software Fault Localization Based on Weighted Association Rule Mining and Complex Networks
by Wentao Wu, Shihai Wang and Bin Liu
Mathematics 2024, 12(13), 2113; https://doi.org/10.3390/math12132113 - 5 Jul 2024
Viewed by 596
Abstract
Software fault localization technology aims to identify suspicious statements that cause software failures, which is crucial for ensuring software quality. Spectrum-based software fault location (SBFL) technology calculates the suspiciousness of each statement by analyzing the correlation between statement coverage information and execution results [...] Read more.
Software fault localization technology aims to identify suspicious statements that cause software failures, which is crucial for ensuring software quality. Spectrum-based software fault location (SBFL) technology calculates the suspiciousness of each statement by analyzing the correlation between statement coverage information and execution results in test cases. SBFL has attracted increasing attention from scholars due to its high efficiency and scalability. However, existing SBFL studies have shown that a large number of statements share the same suspiciousness, which hinders software debuggers from quickly identifying the location of faulty statements. To address this challenge, we propose an SBFL model based on weighted association rule mining and complex networks: FL-WARMCN. The algorithm first uses Jaccard to measure the distance between passing and failing test cases, and applies it as the weight of passing test cases. Next, FL-WARMCN calculates the initial suspiciousness of each statement based on the program spectrum data. Then, the FL-WARMCN model utilizes a weighted association rule mining algorithm to obtain the correlation relationships between statements and models the network based on this. In the network, the suspiciousness of statements is used as node weights, and the correlation between statements is used as edge weights. We chose the eigenvector centrality that takes into account the degree centrality of statements and the importance of neighboring statements to calculate the importance of each statement, and used it as a weight to incorporate into the weighted suspiciousness calculation of the statement. Finally, we applied the FL-WARMCN model for experimental validation on the Defects4J dataset. The results showed that the model was significantly superior to other baselines. In addition, we analyzed the impact of different node and edge weights on model performance. Full article
(This article belongs to the Special Issue Advances in Mathematics Computation for Software Engineering)
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