Improving the Condensing of Reverse Engineered Class Diagrams using Weighted Network Metrics
Author:
Affiliation:
1. Zhejiang Gongshang University, Zhejiang, China
2. Oakland University, Rochester, Michigan, USA
Funder
National Natural Science Foundation of China
Natural Science Foundation of Zhejiang Province
Zhejiang Gongshang University ?Digital+ Disciplinary Construction Management Project
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3639478.3643520
Reference8 articles.
1. Empirical analysis of network measures for effort-aware fault-proneness prediction
2. Mohd Hafeez Osman, Michel R. V. Chaudron, and Peter van der Putten. 2013. An Analysis of Machine Learning Algorithms for Condensing Reverse Engineered Class Diagrams. In 2013 IEEE International Conference on Software Maintenance, Eindhoven, The Netherlands, September 22--28, 2013. IEEE Computer Society, 140--149.
3. EASE: An effort-aware extension of unsupervised key class identification approaches
4. Pride: Prioritizing Documentation Effort Based on a PageRank-Like Algorithm and Simple Filtering Rules
5. Impact of Discretization Noise of the Dependent Variable on Machine Learning Classifiers in Software Engineering
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