2012 |
Ferrer, Javier; Chicano, Francisco; Alba, Enrique Evolutionary algorithms for the multi-objective test data generation problem Journal Article Software: Practice and Experience, 42 (11), pp. 1331–1362, 2012, ISSN: 00380644. Abstract | Links | BibTeX | Tags: branch coverage, evolutionary algorithms, evolutionary testing, multi-objective test data generation, oracle cost, Search-based Software Engineering @article{Ferrer2012a, title = {Evolutionary algorithms for the multi-objective test data generation problem}, author = {Javier Ferrer and Francisco Chicano and Enrique Alba}, url = {http://doi.wiley.com/10.1002/spe.1135 http://arxiv.org/abs/1008.1900}, doi = {10.1002/spe.1135}, issn = {00380644}, year = {2012}, date = {2012-11-01}, journal = {Software: Practice and Experience}, volume = {42}, number = {11}, pages = {1331--1362}, abstract = {Cloud computing promises a radical shift in the provisioning of computing resource within the enterprise. This paper describes the challenges that decision makers face when assessing the feasibility of the adoption of cloud computing in their organisations, and describes our Cloud Adoption Toolkit, which has been developed to support this process. The toolkit provides a framework to support decision makers in identifying their concerns, and matching these concerns to appropriate tools/techniques that can be used to address them. Cost Modeling is the most mature tool in the toolkit, and this paper shows its effectiveness by demonstrating how practitioners can use it to examine the costs of deploying their IT systems on the cloud. The Cost Modeling tool is evaluated using a case study of an organization that is considering the migration of some of its IT systems to the cloud. The case study shows that running systems on the cloud using a traditional "always on" approach can be less cost effective, and the elastic nature of the cloud has to be used to reduce costs. Therefore, decision makers have to be able to model the variations in resource usage and their systems deployment options to obtain accurate cost estimates.}, keywords = {branch coverage, evolutionary algorithms, evolutionary testing, multi-objective test data generation, oracle cost, Search-based Software Engineering}, pubstate = {published}, tppubtype = {article} } Cloud computing promises a radical shift in the provisioning of computing resource within the enterprise. This paper describes the challenges that decision makers face when assessing the feasibility of the adoption of cloud computing in their organisations, and describes our Cloud Adoption Toolkit, which has been developed to support this process. The toolkit provides a framework to support decision makers in identifying their concerns, and matching these concerns to appropriate tools/techniques that can be used to address them. Cost Modeling is the most mature tool in the toolkit, and this paper shows its effectiveness by demonstrating how practitioners can use it to examine the costs of deploying their IT systems on the cloud. The Cost Modeling tool is evaluated using a case study of an organization that is considering the migration of some of its IT systems to the cloud. The case study shows that running systems on the cloud using a traditional "always on" approach can be less cost effective, and the elastic nature of the cloud has to be used to reduce costs. Therefore, decision makers have to be able to model the variations in resource usage and their systems deployment options to obtain accurate cost estimates. |
2012 |
Evolutionary algorithms for the multi-objective test data generation problem Journal Article Software: Practice and Experience, 42 (11), pp. 1331–1362, 2012, ISSN: 00380644. |