Classifying Feature Models Maintainability based on Machine Learning Algorithms

Author:

Silva Publio1,Bezerra Carla I. M.1,Lima Rafael1,Machado Ivan2

Affiliation:

1. Federal University of Ceara, Quixada, Brazil

2. Federal University of Bahia, Salvador, Brazil

Publisher

ACM

Reference20 articles.

1. Charu C. Aggarwal and Chandan K. Reddy. 2013. Data Clustering: Algorithms and Applications (1st ed.). Chapman and Hall/CRC. Charu C. Aggarwal and Chandan K. Reddy. 2013. Data Clustering: Algorithms and Applications (1st ed.). Chapman and Hall/CRC.

2. Mohamed Alloghani Dhiya Al-Jumeily Jamila Mustafina Abir Hussain and Ahmed J. Aljaaf. 2020. A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science. Springer International Publishing Cham 3--21. 10.1007/978-3-030-22475-2_1 Mohamed Alloghani Dhiya Al-Jumeily Jamila Mustafina Abir Hussain and Ahmed J. Aljaaf. 2020. A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science. Springer International Publishing Cham 3--21. 10.1007/978-3-030-22475-2_1

3. Threshold Extraction Framework for Software Metrics;Alqmase Mohammed;Journal of Computer Science and Technology,2019

4. What software reuse benefits have been transferred to the industry? A systematic mapping study

5. Exploring quality measures for the evaluation of feature models: a case study

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2. Automating Feature Model maintainability evaluation using machine learning techniques;Journal of Systems and Software;2023-01

3. DyMMer 2.0: A Tool for Dynamic Modeling and Evaluation of Feature Model;Brazilian Symposium on Software Engineering;2021-09-27

4. A machine learning model to classify the feature model maintainability;Proceedings of the 25th ACM International Systems and Software Product Line Conference - Volume A;2021-09-06

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