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Electronics
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Machine Learning Methods in Software Engineering
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Machine Learning Methods in Software Engineering

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

Deadline for manuscript submissions: closed (15 July 2024) | Viewed by 12155

Special Issue Editors

Dr. Radek Silhavy

E-Mail Website
Guest Editor
Department of Computer and Communication Systems, Faculty of Applied Informatics, Tomas Bata University in Zlin, 76001 Zlin, Czech Republic
Interests: empirical software engineering; software size estimation; machine learning; statistical learning
Dr. Petr Silhavy

E-Mail Website
Guest Editor
Department of Computer and Communication Systems, Faculty of Applied Informatics, Tomas Bata University in Zlin, 76001 Zlin, Czech Republic
Interests: database systems; software engineering; machine learning
Prof. Dr. Luiz Fernando Capretz

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Faculty of Engineering, Western University, London, ON, Canada
Interests: software verification and validation; predictive models human factors in software engineering; software testing; engineering education
Dr. Ali Bou Nassif

E-Mail Website
Guest Editor
Department of Computer Engineering, College of Computing and Informatics, University of Sharjah, Sharjah, United Arab Emirates
Interests: software cost estimation; artificial intelligence; deep learning; speech recognition

Special Issue Information

Dear Colleagues,

Currently, the modern digital economy and society rely on software systems. Many software development projects fail or struggle to finish on time within budget.

Software engineering is challenged with fast changes in project objectives, constraints, or priorities. Changes in competitive threats, technology, organizations, leadership priorities, and environments must be incorporated into the software engineering process. The involvement of machine learning can improve strategies such as incremental and evolutionary development, which brings new issues from requirements to the sizing of new projects. Contributions scope may include topics such as:

  • neural networks, including a deep neural network in software engineering;
  • deep learning and other artificial algorithms for predictions in software engineering;
  • clustering methods in software engineering;
  • bio-inspired algorithms and their application;
  • fuzzy sets;
  • machine learning and AI application in project effort estimation;
  • mathematical statistics and AI applications in testing and software quality.

The contribution may be related to the whole software development lifecycle.

Dr. Radek Silhavy
Dr. Petr Silhavy
Prof. Dr. Luiz Fernando Capretz
Dr. Ali Bou Nassif
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

  • software engineering
  • deep learning
  • bioinspired methods
  • empirical research in software engineering
  • machine learning
  • computational methods

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Published Papers (6 papers)

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Research

20 pages, 6444 KiB  
Article
Efficient Cross-Project Software Defect Prediction Based on Federated Meta-Learning
by Haisong Chen, Linlin Yang and Aili Wang
Electronics 2024, 13(6), 1105; https://doi.org/10.3390/electronics13061105 - 18 Mar 2024
Viewed by 1205
Abstract
Software defect prediction is an important part of software development, which aims to use existing historical data to predict future software defects. Focusing on the model performance and communication efficiency of cross-project software defect prediction, this paper proposes an efficient communication-based federated meta-learning [...] Read more.
Software defect prediction is an important part of software development, which aims to use existing historical data to predict future software defects. Focusing on the model performance and communication efficiency of cross-project software defect prediction, this paper proposes an efficient communication-based federated meta-learning (ECFML) algorithm. The lightweight MobileViT network is used as the meta-learner of the Model Agnostic Meta-Learning (MAML) algorithm. By learning common knowledge on the local data of multiple clients, and then fine-tuning the model, the number of unnecessary iterations is reduced, and communication efficiency is improved while reducing the number of parameters. The gradient information model is encrypted using the differential privacy of the Laplace mechanism, and the optimal privacy budget is determined through experiments. Experiments on three public datasets (AEEEM, NASA, and Relink) verified the effectiveness of ECFML in terms of parameter quantity, convergence, and model performance of cross-project software defect prediction. Full article
(This article belongs to the Special Issue Machine Learning Methods in Software Engineering)
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26 pages, 1118 KiB  
Article
Variability Management in Self-Adaptive Systems through Deep Learning: A Dynamic Software Product Line Approach
by Oscar Aguayo, Samuel Sepúlveda and Raúl Mazo
Electronics 2024, 13(5), 905; https://doi.org/10.3390/electronics13050905 - 27 Feb 2024
Cited by 1 | Viewed by 1144
Abstract
Self-adaptive systems can autonomously adjust their behavior in response to environmental changes. Nowadays, not only can these systems be engineered individually, but they can also be conceived as members of a family based on the approach of dynamic software product lines. Through systematic [...] Read more.
Self-adaptive systems can autonomously adjust their behavior in response to environmental changes. Nowadays, not only can these systems be engineered individually, but they can also be conceived as members of a family based on the approach of dynamic software product lines. Through systematic mapping, we build on the identified gaps in the variability management of self-adaptive systems; we propose a framework that improves the adaptive capability of self-adaptive systems through feature model generation, variation point generation, the selection of a variation point, and runtime variability management using deep learning and the monitor–analysis–plan–execute–knowledge (MAPE-K) control loop. We compute the permutation of domain features and obtain all the possible variation points that a feature model can possess. After identifying variation points, we obtain an adaptation rule for each variation point of the corresponding product line through a two-stage training of an artificial neural network. To evaluate our proposal, we developed a test case in the context of an air quality-based activity recommender system, in which we generated 11 features and 32 possible variations. The results obtained with the proof of concept show that it is possible to manage identifying new variation points at runtime using deep learning. Future research will employ generating and building variation points using artificial intelligence techniques. Full article
(This article belongs to the Special Issue Machine Learning Methods in Software Engineering)
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19 pages, 1261 KiB  
Article
Software Requirement Risk Prediction Using Enhanced Fuzzy Induction Models
by Hussaini Mamman, Abdullateef Oluwagbemiga Balogun, Shuib Basri, Luiz Fernando Capretz, Victor Elijah Adeyemo, Abdullahi Abubakar Imam and Ganesh Kumar
Electronics 2023, 12(18), 3805; https://doi.org/10.3390/electronics12183805 - 8 Sep 2023
Cited by 4 | Viewed by 1216
Abstract
The development of most modern software systems is accompanied by a significant level of uncertainty, which can be attributed to the unanticipated activities that may occur throughout the software development process. As these modern software systems become more complex and drawn out, escalating [...] Read more.
The development of most modern software systems is accompanied by a significant level of uncertainty, which can be attributed to the unanticipated activities that may occur throughout the software development process. As these modern software systems become more complex and drawn out, escalating software project failure rates have become a critical concern. These unforeseeable uncertainties are known as software risks, and they emerge from many risk factors inherent to the numerous activities comprising the software development lifecycle (SDLC). Consequently, these software risks have resulted in massive revenue losses for software organizations. Hence, it is imperative to address these software risks, to curb future software system failures. The subjective risk assessment (SRM) method is regarded as a viable solution to software risk problems. However, it is inherently reliant on humans and, therefore, in certain situations, imprecise, due to its dependence on an expert’s knowledge and experience. In addition, the SRM does not allow repeatability, as expertise is not easily exchanged across the different units working on a software project. Developing intelligent modelling methods that may offer more unbiased, reproducible, and explainable decision-making assistance in risk management is crucial. Hence, this research proposes enhanced fuzzy induction models for software requirement risk prediction. Specifically, the fuzzy unordered rule induction algorithm (FURIA), and its enhanced variants based on nested subset selection dichotomies, are developed for software requirement risk prediction. The suggested fuzzy induction models are based on the use of effective rule-stretching methods for the prediction process. Additionally, the proposed FURIA method is enhanced through the introduction of nested subset selection dichotomy concepts into its prediction process. The prediction performances of the proposed models are evaluated using a benchmark dataset, and are then compared with existing machine learning (ML)-based and rule-based software risk prediction models. From the experimental results, it was observed that the FURIA performed comparably, in most cases, to the rule-based and ML-based models. However, the FURIA nested dichotomy variants were superior in performance to the conventional FURIA method, and rule-based and ML-based methods, with the least accuracy, area under the curve (AUC), and Mathew’s correlation coefficient (MCC), with values of approximately 98%. Full article
(This article belongs to the Special Issue Machine Learning Methods in Software Engineering)
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14 pages, 637 KiB  
Article
A Multi-Feature Fusion-Based Automatic Detection Method for High-Severity Defects
by Jie Liu, Cangming Liang, Jintao Feng, Anhong Xiao, Hui Zeng, Qujin Wu and Tonglan Yu
Electronics 2023, 12(14), 3075; https://doi.org/10.3390/electronics12143075 - 14 Jul 2023
Viewed by 963
Abstract
It is crucial to detect high-severity defects, such as memory leaks that can result in system crashes or severe resource depletion, in order to reduce software development costs and ensure software quality and reliability. The primary cause of high-severity defects is usually resource [...] Read more.
It is crucial to detect high-severity defects, such as memory leaks that can result in system crashes or severe resource depletion, in order to reduce software development costs and ensure software quality and reliability. The primary cause of high-severity defects is usually resource scheduling errors, and in the program source code, these defects have contextual features that require defect context to confirm their existence. In the context of utilizing machine learning methods for defect automatic confirmation, the single-feature label method cannot achieve high-precision defect confirmation results for high-severity defects. Therefore, a multi-feature fusion defect automatic confirmation method is proposed. The label generation method solves the dimensionality disaster problem caused by multi-feature fusion by fusing features with strong correlations, improving the classifier’s performance. This method extracts node features and basic path features from the program dependency graph and designs high-severity contextual defect confirmation labels combined with contextual features. Finally, an optimized Support Vector Machine is used to train the automatic detection model for high-severity defects. This study uses open-source programs to manually implant defects for high-severity defect confirmation verification. The experimental results show that compared with existing methods, this model significantly improves the efficiency of confirming high-severity defects. Full article
(This article belongs to the Special Issue Machine Learning Methods in Software Engineering)
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18 pages, 1553 KiB  
Article
A Novel Source Code Clone Detection Method Based on Dual-GCN and IVHFS
by Haixin Yang, Zhen Li and Xinyu Guo
Electronics 2023, 12(6), 1315; https://doi.org/10.3390/electronics12061315 - 9 Mar 2023
Cited by 1 | Viewed by 2496
Abstract
Source code clone detection, which can identify code fragments with similar functions, plays a significant role in software development and quality assurance. Existing methods either extract single syntactic or semantic information, or ignore the associated information between code statements in different structures. It [...] Read more.
Source code clone detection, which can identify code fragments with similar functions, plays a significant role in software development and quality assurance. Existing methods either extract single syntactic or semantic information, or ignore the associated information between code statements in different structures. It is difficult for these methods to effectively detect clone pairs with similar functions. In this paper, we propose a new model based on a dual graph convolutional network (GCN) and interval-valued hesitant fuzzy set (IVHFS), which we named DG-IVHFS. Specifically, we simplified and grouped the abstract syntax tree (AST) of source code to obtain the group representations. The group representations of the AST, as well as the control flow graph (CFG) representations, were transformed into graph structures, and then we applied GCNs on them to learn dependencies between nodes. In addition, we introduced IVHFS into the model for a more comprehensive evaluation of similarity. Our experimental results demonstrated that the precision, recall, and F1-scores of DG-IVHFS on the BigCloneBench and GoogleCodeJam datasets reached 98, 97 and 97% and 98, 93 and 95%, respectively, exceeding current state-of-the-art models. Moreover, our model performed well in terms of time consumption. Full article
(This article belongs to the Special Issue Machine Learning Methods in Software Engineering)
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18 pages, 6213 KiB  
Article
Multi-Scale Fully Convolutional Network-Based Semantic Segmentation for Mobile Robot Navigation
by Thai-Viet Dang and Ngoc-Tam Bui
Electronics 2023, 12(3), 533; https://doi.org/10.3390/electronics12030533 - 20 Jan 2023
Cited by 37 | Viewed by 3937
Abstract
In computer vision and mobile robotics, autonomous navigation is crucial. It enables the robot to navigate its environment, which consists primarily of obstacles and moving objects. Robot navigation employing impediment detections, such as walls and pillars, is not only essential but also challenging [...] Read more.
In computer vision and mobile robotics, autonomous navigation is crucial. It enables the robot to navigate its environment, which consists primarily of obstacles and moving objects. Robot navigation employing impediment detections, such as walls and pillars, is not only essential but also challenging due to real-world complications. This study provides a real-time solution to the problem of obtaining hallway scenes from an exclusive image. The authors predict a dense scene using a multi-scale fully convolutional network (FCN). The output is an image with pixel-by-pixel predictions that can be used for various navigation strategies. In addition, a method for comparing the computational cost and precision of various FCN architectures using VGG-16 is introduced. The binary semantic segmentation and optimal obstacle avoidance navigation of autonomous mobile robots are two areas in which our method outperforms the methods of competing works. The authors successfully apply perspective correction to the segmented image in order to construct the frontal view of the general area, which identifies the available moving area. The optimal obstacle avoidance strategy is comprised primarily of collision-free path planning, reasonable processing time, and smooth steering with low steering angle changes. Full article
(This article belongs to the Special Issue Machine Learning Methods in Software Engineering)
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