Predicting successful software reuse using machine learning
Predicting successful software reuse using machine learning
- Author(s): M. Amin 1 and M. Hammad 1
- DOI: 10.1049/icp.2021.0873
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- Author(s): M. Amin 1 and M. Hammad 1
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View affiliations
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Affiliations:
1:
Department of Computer Science , College of Information Technology, University of Bahrain
Source:
3rd Smart Cities Symposium (SCS 2020),
2021
p.
1 – 5
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Affiliations:
1:
Department of Computer Science , College of Information Technology, University of Bahrain
- Conference: 3rd Smart Cities Symposium (SCS 2020)
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- DOI: 10.1049/icp.2021.0873
- ISBN: 978-1-83953-522-2
- Location: Online Conference
- Conference date: 21-23 September 2020
- Format: PDF
Nowadays, software is developed from reused components to increase productivity. Predicting the success of reuse helps in assessing component’s reuse potential. In this paper, six machine-learning algorithms were used to predict successful software reuse. Naive Bayes, artificial neural network, K-Star, Hoeffding tree, support vector machine, and k-nearest neighbor were used to evaluate a public dataset. Results showed that machine learning performed very well in predicting software reuse with accuracy reaches 100%.
Inspec keywords: software reusability; neural nets; support vector machines; learning (artificial intelligence); naive Bayes methods; decision trees; nearest neighbour methods
Subjects: Software engineering techniques; Support vector machines; Other learning models (inc. Naive Bayes); Combinatorial mathematics; Other topics in statistics; Neural nets