Software architecture: perspectives on an emerging discipline
Software architecture: perspectives on an emerging discipline
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Using Heuristic Search Techniques To Extract Design Abstractions From Source Code
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
A New Representation And Crossover Operator For Search-based Optimization Of Software Modularization
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
ACDC: An Algorithm for Comprehension-Driven Clustering
WCRE '00 Proceedings of the Seventh Working Conference on Reverse Engineering (WCRE'00)
ICSM '01 Proceedings of the IEEE International Conference on Software Maintenance (ICSM'01)
Using Automatic Clustering to Produce High-Level System Organizations of Source Code
IWPC '98 Proceedings of the 6th International Workshop on Program Comprehension
A Multiple Hill Climbing Approach to Software Module Clustering
ICSM '03 Proceedings of the International Conference on Software Maintenance
Practical Genetic Algorithms with CD-ROM
Practical Genetic Algorithms with CD-ROM
Information-Theoretic Software Clustering
IEEE Transactions on Software Engineering
Search-Based Amorphous Slicing
WCRE '05 Proceedings of the 12th Working Conference on Reverse Engineering
Search-Based Software Maintenance
CSMR '06 Proceedings of the Conference on Software Maintenance and Reengineering
On the Automatic Modularization of Software Systems Using the Bunch Tool
IEEE Transactions on Software Engineering
A novel approach to optimize clone refactoring activity
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Simulated annealing for improving software quality prediction
Proceedings of the 8th annual conference on Genetic and evolutionary computation
| Hi-index | 0.00 |
Maintenance is one of the important phases of the software development life cycle which contributes to the effective and long term use of any software system. It can become cumbersome and costly for software maintainers when subsystem boundaries are not clearly defined. Further, the problem gets worse due to the system evolution, lack of current documentation and lack of original design documentation. The application of clustering techniques and tools helps software maintenance programmers to recover high-level views of system designs and hence leads to better understanding and maintenance of software systems. In this paper, we have used a sociopolitical Imperialist Competitive Algorithm for software module clustering and compared it with existing evolutionary approaches. We conclude that the novel socio-political approach produces better quality clusters as compared to the earlier evolutionary genetic approaches.