Concept Location with Genetic Algorithms: A Comparison of Four Distributed Architectures. Asadi, F., Antoniol, G., & Gu�h�neuc, Y. In Proceedings of the 2<sup>nd</sup> International Symposium on Search Based Software Engineering (SSBSE), pages 153–162, September, 2010. IEEE CS Press. 10 pages.
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Paper abstract bibtex
Paper abstract bibtex Genetic algorithms are attractive to solve many search-based software engineering problems because they allow the easy parallelization of computations, which improves scalability and reduces computation time. In this paper, we present our experience in applying different distributed architectures to parallelize a genetic algorithm used to solve the concept identification problem. We developed an approach to identify concepts in execution traces by finding cohesive and decoupled fragments of the traces. The approach relies on a genetic algorithm, on a textual analysis of source code using latent semantic indexing, and on trace compression techniques. The fitness function in our approach has a polynomial evaluation cost and is highly computationally intensive. A run of our approach on a trace of thousand methods may require several hours of computation on a standard PC. Consequently, we reduced computation time by parallelizing the genetic algorithm at the core of our approach over a standard TCP/IP network. We developed four distributed architectures and compared their performances: we observed a decrease of computation time up to 140 times. Although presented in the context of concept location, our findings could be applied to many other search-based software engineering problems.
@INPROCEEDINGS{Asadi10-SSBSE-ComparisonDistributedArchitectures,
AUTHOR = {Fatemeh Asadi and Giuliano Antoniol and
Yann-Ga�l Gu�h�neuc},
BOOKTITLE = {Proceedings of the 2<sup>nd</sup> International Symposium on Search Based Software Engineering (SSBSE)},
TITLE = {Concept Location with Genetic Algorithms: A Comparison
of Four Distributed Architectures},
YEAR = {2010},
OPTADDRESS = {},
OPTCROSSREF = {},
EDITOR = {Lionel Briand},
MONTH = {September},
NOTE = {10 pages.},
OPTNUMBER = {},
OPTORGANIZATION = {},
PAGES = {153--162},
PUBLISHER = {IEEE CS Press},
OPTSERIES = {},
OPTVOLUME = {},
KEYWORDS = {Topic: <b>Requirements and features</b>,
Venue: <c>SSBSE</c>},
URL = {http://www.ptidej.net/publications/documents/SSBSE10.doc.pdf},
PDF = {http://www.ptidej.net/publications/documents/SSBSE10.ppt.pdf},
ABSTRACT = {Genetic algorithms are attractive to solve many
search-based software engineering problems because they allow the
easy parallelization of computations, which improves scalability and
reduces computation time. In this paper, we present our experience in
applying different distributed architectures to parallelize a genetic
algorithm used to solve the concept identification problem. We
developed an approach to identify concepts in execution traces by
finding cohesive and decoupled fragments of the traces. The approach
relies on a genetic algorithm, on a textual analysis of source code
using latent semantic indexing, and on trace compression techniques.
The fitness function in our approach has a polynomial evaluation cost
and is highly computationally intensive. A run of our approach on a
trace of thousand methods may require several hours of computation on
a standard PC. Consequently, we reduced computation time by
parallelizing the genetic algorithm at the core of our approach over
a standard TCP/IP network. We developed four distributed
architectures and compared their performances: we observed a decrease
of computation time up to 140 times. Although presented in the
context of concept location, our findings could be applied to many
other search-based software engineering problems.}
}Downloads: 0
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