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: 15 July 2024 | Viewed by 9006
Special Issue Editors
Interests: empirical software engineering; software size estimation; machine learning; statistical learning
Interests: database systems; software engineering; machine learning
Interests: software verification and validation; predictive models human factors in software engineering; software testing; engineering education
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
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Keywords
- software engineering
- deep learning
- bioinspired methods
- empirical research in software engineering
- machine learning
- computational methods