Spectral and meta-heuristic algorithms for software clustering
Journal of Systems and Software - Special issue: Software reverse engineering
Reducing internal fragmentation in segregated free lists using genetic algorithms
Proceedings of the 2006 international workshop on Workshop on interdisciplinary software engineering research
Getting the most from search-based refactoring
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Search-based refactoring for software maintenance
Journal of Systems and Software
Search-based refactoring: an empirical study
Journal of Software Maintenance and Evolution: Research and Practice - Search Based Software Engineering [SBSE]
Automated Design Improvement by Example
Proceedings of the 2007 conference on New Trends in Software Methodologies, Tools and Techniques: Proceedings of the sixth SoMeT_07
The relationship between search based software engineering and predictive modeling
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
Highly scalable multi objective test suite minimisation using graphics cards
SSBSE'11 Proceedings of the Third international conference on Search based software engineering
Search based software engineering: techniques, taxonomy, tutorial
Empirical Software Engineering and Verification
Search-based software engineering: Trends, techniques and applications
ACM Computing Surveys (CSUR)
| Hi-index | 0.00 |
Collections of general purpose networked workstations offer processing capability that often rivals or exceeds supercomputers. Since networked workstations are readily available in most organizations, they provide an economic and scalable alternative to parallel machines. In this paper we discuss how individual nodes in computer network can be used as a collection of connected processing elements to improve the performance of software engineering tool that we developed. Our tool, called Bunch, automatically clusters the structure of software systems into hierarchy of subsystems. Clustering helps developers understand complex systems by providing them with high-level abstract (clustered)views of the software structure. The algorithms used by Bunch are computationally intensive and, hence, we would like to improve our tool's performance in order to cluster very large systems. This paper describes how we designed and implemented distributed version of Bunch, which is useful for clustering large systems.