Software Module Clustering as a Multi-Objective Search Problem
By Kata Praditwong, Mark Harman, Xin Yao. IEEE Transactions on Software Engineering, 2011
400 citations recorded by the source corpus.
Subfields: Computer Science Applications, Information Systems
View Paper: https://www.semanticscholar.org/paper/93b905cfe789bc9071c6b9df9b2f4a81a55c91e2
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Science Applications
- Yuanyuan Zhang
- S. Afshin Mansouri
- Mark Harman
- Xin Yao
- Phil McMinn
- Westley Weimer
- Walid Maalej
- Koushik Sen
In Information Systems
- Mark Harman
- Phil McMinn
- Shin Yoo
- Yuanyuan Zhang
- S. Afshin Mansouri
- Jerffeson Teixeira de Souza
- Westley Weimer
- Xin Yao
Citations in the corpus
- Search-based software engineering: Trends, techniques and applications (2012, positive): “[Praditwong et al. 2010] recently demonstrated that multi-objective optimization can significantly outperform HC in terms of modularisation quality.”
- A New Dominance Relation-Based Evolutionary Algorithm for Many-Objective Optimization (2016, background): “, control system design [3], [4], industrial scheduling [5], [6], and software engineering [7], [8].”
- Balancing Convergence and Diversity in Decomposition-Based Many-Objective Optimizers (2016, background): “However, most of these algorithms have been evaluated and applied to MOPs with only two or three objectives [1], [2], despite the fact that optimization problems involving a high number of objectives indeed appear widely in real-world applications [3]–[5].”
- Genetic programming for Reverse Engineering (2013, background): “[77] yielded good quality, but they came at a high computational price; the multi objective search required two orders of magnitude more time than the humble hill climber.”
- Search Based Software Engineering: Techniques, Taxonomy, Tutorial (2010, methodology): “Furthermore, the multi objective genetic algorithm that found better solutions only managed to do so at a cost of two orders of magnitude more fitness evaluations [77].”
- Many-Objective Evolutionary Algorithms (2015, methodology): “This approach was also applied to real-world problems in software engineering [Praditwong et al. 2011] and in sensornet protocol optimization [Tate et al. 2012].”