Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.5
2.4
Journals
Mathematics
Special Issues
Applications of Soft Computing in Software Engineering
share announcement

Applications of Soft Computing in Software Engineering

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

Deadline for manuscript submissions: closed (28 February 2024) | Viewed by 4370

Special Issue Editors

Prof. Dr. Vassilis C. Gerogiannis

E-Mail Website
Guest Editor
Department of Digital Systems, Faculty of Technology, University of Thessaly, Geopolis Campus, GR 41500 Larissa, Greece
Interests: fuzzy decision making; software engineering; requirements engineering; systems analysis and design; machine learning software product development processes
Special Issues, Collections and Topics in MDPI journals
Dr. Andreas Kanavos

E-Mail Website
Guest Editor
Department of Informatics, Ionian University, Corfu, Greece
Interests: artificial intelligence; machine learning; data management and mining; big data; social network analysis; information retrieval
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The fields of Soft Engineering and Soft Computing are vast, versatile, and fascinating. Software-intensive systems are becoming more and more complex and thus, we need more intelligent approaches to solve several challenging problems in this domain. According to recent studies, soft computing has become an important field in the development of software engineering methods. Soft Computing is a collection of methods that try to cope with the main disadvantages of the conventional computing, especially the poor performances when working under imprecision, uncertainty, partial truth, and approximation conditions. The classic constituents of Soft Computing contain fuzzy sets and fuzzy systems, probabilistic reasoning, support vector machines, belief networks, artificial neural networks, fuzzy cognitive maps as well as evolutionary computation based on genetic algorithms, swarm intelligence methods, and other nature-inspired computation paradigms. The main focus of this special issue is to demonstrate the application of different Soft Computing methodologies on various software engineering problems.

Topics of this special issue include (but not limited to):

  • 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
  • Genetic Algorithms
  • Multi-Objective Evolutionary Computation
  • Soft Computing for Big Data Era
  • Intelligent Software Agent Systems and Architectures
  • Chaos Theory
  • Other issues related to the advances of Soft Computing in various applications

Related application fields of Soft Computing in Software Engineering include (but not limited to):

  • 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
  • Reusability, Maintainability and Testability Prediction
  • Quality and Vulnerability Prediction

Prof. Dr. Vassilis C. Gerogiannis
Dr. Andreas Kanavos
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.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

18 pages, 1047 KiB  
Article
Software Estimation in the Design Stage with Statistical Models and Machine Learning: An Empirical Study
by Ángel J. Sánchez-García, María Saarayim González-Hernández, Karen Cortés-Verdín and Juan Carlos Pérez-Arriaga
Mathematics 2024, 12(7), 1058; https://0-doi-org.brum.beds.ac.uk/10.3390/math12071058 - 01 Apr 2024
Viewed by 470
Abstract
Accurate estimation of software effort and time in the software development process is a key activity to achieve the necessary product quality. However, underestimation or overestimation of effort has become a key challenge for software development. One of the main problems is the [...] Read more.
Accurate estimation of software effort and time in the software development process is a key activity to achieve the necessary product quality. However, underestimation or overestimation of effort has become a key challenge for software development. One of the main problems is the estimation with metrics from late stages, because the product must already be finished to make estimates. In this paper, the use of statistical models and machine learning approaches for software estimation are used in early stages such as software design, and a data set is presented with metric values of design artifacts with 37 software projects. As results, models for the estimation of development time and effort are proposed and validated through leave-one-out cross-validation. Further, machine learning techniques were employed in order to compare software projects estimations. Through the statistical tests, it was proven that the errors were not statistically different with the regression models for effort estimation. However, with Random Forest the best statistical results were obtained for estimating development time. Full article
(This article belongs to the Special Issue Applications of Soft Computing in Software Engineering)
►▼ Show Figures

Figure 1

36 pages, 721 KiB  
Article
An Approach Based on Intuitionistic Fuzzy Sets for Considering Stakeholders’ Satisfaction, Dissatisfaction, and Hesitation in Software Features Prioritization
by Vassilis C. Gerogiannis, Dimitrios Tzimos, George Kakarontzas, Eftychia Tsoni, Omiros Iatrellis, Le Hoang Son and Andreas Kanavos
Mathematics 2024, 12(5), 680; https://0-doi-org.brum.beds.ac.uk/10.3390/math12050680 - 26 Feb 2024
Viewed by 1567
Abstract
This paper introduces a semi-automated approach for the prioritization of software features in medium- to large-sized software projects, considering stakeholders’ satisfaction and dissatisfaction as key criteria for the incorporation of candidate features. Our research acknowledges an inherent asymmetry in stakeholders’ evaluations, between the [...] Read more.
This paper introduces a semi-automated approach for the prioritization of software features in medium- to large-sized software projects, considering stakeholders’ satisfaction and dissatisfaction as key criteria for the incorporation of candidate features. Our research acknowledges an inherent asymmetry in stakeholders’ evaluations, between the satisfaction from offering certain features and the dissatisfaction from not offering the same features. Even with systematic, ordinal scale-based prioritization techniques, involved stakeholders may exhibit hesitation and uncertainty in their assessments. Our approach aims to address these challenges by employing the Binary Search Tree prioritization method and leveraging the mathematical framework of Intuitionistic Fuzzy Sets to quantify the uncertainty of stakeholders when expressing assessments on the value of software features. Stakeholders’ rankings, considering satisfaction and dissatisfaction as features prioritization criteria, are mapped into Intuitionistic Fuzzy Numbers, and objective weights are automatically computed. Rankings associated with less hesitation are considered more valuable to determine the final features’ priorities than those rankings with more hesitation, reflecting lower indeterminacy or lack of knowledge from stakeholders. We validate our proposed approach with a case study, illustrating its application, and conduct a comparative analysis with existing software requirements prioritization methods. Full article
(This article belongs to the Special Issue Applications of Soft Computing in Software Engineering)
►▼ Show Figures

Figure 1

16 pages, 3821 KiB  
Article
Analysis, Evaluation and Reusability of Virtual Laboratory Software Based on Conceptual Modeling and Conformance Checking
by Athanasios Sypsas and Dimitris Kalles
Mathematics 2023, 11(9), 2153; https://0-doi-org.brum.beds.ac.uk/10.3390/math11092153 - 04 May 2023
Viewed by 1322
Abstract
Virtual laboratories have been increasingly used in tertiary education for natural and applied sciences, especially due to the COVID pandemic, generating a substantial investment in corresponding software applications, including simulation experiments and procedures. However, it is expensive and time-consuming to analyze, understand, model [...] Read more.
Virtual laboratories have been increasingly used in tertiary education for natural and applied sciences, especially due to the COVID pandemic, generating a substantial investment in corresponding software applications, including simulation experiments and procedures. However, it is expensive and time-consuming to analyze, understand, model and implement the virtual experiments, especially when it is necessary to create new ones from scratch, but also when they must be redesigned and addressed to an audience in a different educational setting. We use UML Activity Diagrams and Petri nets to model experimental procedures and then apply conformance checking to detect possible nonconformities between expected model behavior and actual model execution. As a result, we provide an estimation of the conceptual proximity between experiments performed in different educational settings using the same virtual laboratory software, assisting educators and developers in making informed decisions about software reuse and redesign by providing a systematic and formal way of evaluating software applicability. A virtual microscoping experiment was used as a case study for validation purposes. The results revealed that the specific virtual lab software can be ported, without modifications, from tertiary to secondary education, to achieve learning outcomes relevant to that education level, even though it was originally designed for a distance education university. The proposed framework has potential applications beyond virtual laboratories, as a general approach to process modeling and conformance checking to evaluate the similarity between the specification of experimental procedures and actual execution logs can be applied to various domains. Full article
(This article belongs to the Special Issue Applications of Soft Computing in Software Engineering)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop