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Electronics
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Machine Learning (ML) and Software Engineering, 2nd Edition
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Machine Learning (ML) and Software Engineering, 2nd Edition

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

Deadline for manuscript submissions: 15 May 2025 | Viewed by 2804

Special Issue Editors

Dr. Seonah Lee

E-Mail Website1 Website2
Guest Editor
Department of Software Engineering, Gyeongsang National University, 501 Jinjudaero, Jinju-si 52828, Republic of Korea
Interests: software evolution; documentation updates; requirement traceability; software architecture; data mining; machine learning; artificial intelligence; intelligent systems
Special Issues, Collections and Topics in MDPI journals
Dr. Kim Jindae

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea
Interests: automatic program repair; mining software repositories; software evolution

Special Issue Information

Dear Colleagues,

At present, AI, machine learning, and deep learning technologies are remarkably developed, and various intelligent systems are being accordingly developed. This Special Issue aims to present a collection of the various software engineering techniques, tools, and methodologies of such intelligent systems, in addition to AI, machine learning, and deep learning technologies, that can be utilized to support intelligent software engineering. We also welcome topics on AI-based systems, such as autonomous vehicles, autonomous flights, and medical AI. Specific topics of interest include, but are not limited to, the following:

  • Artificial intelligence for software engineering;
  • Software engineering for artificial intelligence;
  • Machine learning for requirement engineering;
  • Machine learning for design;
  • Machine learning for code generation;
  • Machine learning for code example generation;
  • Machine learning for code description;
  • Machine learning for testing;
  • Machine learning for code changes;
  • Machine learning for artifact changes;
  • CI, DevOps, and MLOps for intelligent software systems;
  • Issues on autonomous vehicles;
  • Issues on autonomous flights;
  • Issues on medical AIs.

Through this issue, we will reveal suitable machine learning techniques for supporting intelligent software engineering as well as software engineering methodologies for supporting emerging intelligent software systems. In addition, we would like to foster open discussion on the new horizon of intelligent software systems. Therefore, we welcome various new ideas and their verification in the development of intelligent software systems.

Dr. Seonah Lee
Dr. Kim Jindae
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

  • machine learning
  • software engineering
  • requirement analysis
  • design
  • implementation
  • testing
  • intelligence system
  • recommendation system
  • data mining
  • artificial intelligence

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Related Special Issue

Published Papers (2 papers)

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Research

18 pages, 1050 KiB  
Article
A Test Report Optimization Method Fusing Reinforcement Learning and Genetic Algorithms
by Ruxue Bai, Rongshang Chen, Xiao Lei and Keshou Wu
Electronics 2024, 13(21), 4281; https://doi.org/10.3390/electronics13214281 - 31 Oct 2024
Viewed by 1058
Abstract
Filtering high-variability and high-severity defect reports from large test report databases is a challenging task in crowdtesting. Traditional optimization algorithms based on clustering and distance techniques have made progress but are limited by initial parameter settings and significantly decrease in efficiency with an [...] Read more.
Filtering high-variability and high-severity defect reports from large test report databases is a challenging task in crowdtesting. Traditional optimization algorithms based on clustering and distance techniques have made progress but are limited by initial parameter settings and significantly decrease in efficiency with an increasing number of reports. To address this issue, this paper proposes a method that integrates reinforcement learning with genetic algorithms for crowdsourced testing report optimization, called Reinforcement Learning-based Genetic Algorithm for Crowdsourced Testing Report Optimization (RLGA). Its core goal is to identify distinct, high-severity defect reports from a large set. The method uses genetic algorithms to generate the optimal report selection sequence and adjusts the crossover probability (Pc) and mutation probability (Pm) dynamically with reinforcement learning based on the population’s average fitness, best fitness, and diversity. The reinforcement learning component uses a hybrid SARSA and Q-Learning strategy to update the Q-value table, allowing the algorithm to learn quickly in early iterations and expand the search space later to avoid local optima, thereby improving efficiency. To validate the RLGA method, this paper uses four public datasets and compares RLGA with six classic methods. The results indicate that RLGA outperforms BDDIV in terms of execution time and is less sensitive to the total number of test reports. In terms of optimization objectives, the test reports selected by RLGA have higher levels of defect severity and diversity than those selected by the random choice, BDDIV, and TSE methods. Regarding population diversity, RLGA effectively enhances the uniformity and diversity of individuals compared to random initialization. In terms of convergence speed, RLGA is superior to the GA, GA-SARSA, and GA-Q methods. Full article
(This article belongs to the Special Issue Machine Learning (ML) and Software Engineering, 2nd Edition)
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21 pages, 637 KiB  
Article
Function-Level Compilation Provenance Identification with Multi-Faceted Neural Feature Distillation and Fusion
by Yang Gao, Lunjin Liang, Yifei Li, Rui Li and Yu Wang
Electronics 2024, 13(9), 1692; https://doi.org/10.3390/electronics13091692 - 27 Apr 2024
Viewed by 1196
Abstract
In the landscape of software development, the selection of compilation tools and settings plays a pivotal role in the creation of executable binaries. This diversity, while beneficial, introduces significant challenges for reverse engineers and security analysts in deciphering the compilation provenance of binary [...] Read more.
In the landscape of software development, the selection of compilation tools and settings plays a pivotal role in the creation of executable binaries. This diversity, while beneficial, introduces significant challenges for reverse engineers and security analysts in deciphering the compilation provenance of binary code. To this end, we present MulCPI, short for Multi-representation Fusion-based Compilation Provenance Identification, which integrates the features collected from multiple distinct intermediate representations of the binary code for better discernment of the fine-grained function-level compilation details. In particular, we devise a novel graph-oriented neural encoder improved upon the gated graph neural network by subtly introducing an attention mechanism into the neighborhood nodes’ information aggregation computation, in order to better distill the more informative features from the attributed control flow graph. By further integrating the features collected from the normalized assembly sequence with an advanced Transformer encoder, MulCPI is capable of capturing a more comprehensive set of features manifesting the multi-faceted lexical, syntactic, and structural insights of the binary code. Extensive evaluation on a public dataset comprising 854,858 unique functions demonstrates that MulCPI exceeds the performance of current leading methods in identifying the compiler family, optimization level, compiler version, and the combination of compilation settings. It achieves average accuracy rates of 99.3%, 96.4%, 90.7%, and 85.3% on these tasks, respectively. Additionally, an ablation study highlights the significance of MulCPI’s core designs, validating the efficiency of the proposed attention-enhanced gated graph neural network encoder and the advantages of incorporating multiple code representations. Full article
(This article belongs to the Special Issue Machine Learning (ML) and Software Engineering, 2nd Edition)
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