2020
Ramírez, Aurora; Delgado-Pérez, Pedro; Ferrer, Javier; Romero, José Raúl; Medina-Bulo, Inmaculada; Chicano, Francisco
A systematic literature review of the SBSE research community in Spain Journal Article
In: Progress in Artificial Intelligence, vol. 9, no. 2, pp. 113–128, 2020, ISSN: 21926360.
Abstract | Links | BibTeX | Tags: Research trends, Search-based Software Engineering, Spanish community, Systematic review
@article{Ramirez2020,
title = {A systematic literature review of the SBSE research community in Spain},
author = {Aurora Ramírez and Pedro Delgado-Pérez and Javier Ferrer and José Raúl Romero and Inmaculada Medina-Bulo and Francisco Chicano},
doi = {10.1007/s13748-020-00205-3},
issn = {21926360},
year = {2020},
date = {2020-01-01},
journal = {Progress in Artificial Intelligence},
volume = {9},
number = {2},
pages = {113--128},
abstract = {Since its appearance in 2001, search-based software engineering has allowed software engineers to use optimisation techniques to automate distinctive human problems related to software management and development. The scientific community in Spain has not been alien to these advances. Their contributions cover both the optimisation of software engineering tasks and the proposal of new search algorithms. This review compiles the research efforts of this community in the area. With this aim, we propose a protocol to describe the review process, including the search sources, inclusion and exclusion criteria of candidate papers, the data extraction procedure and the categorisation of primary studies. After retrieving more than 3700 papers, 232 primary studies have been selected, whose analysis gives a precise picture of the current research state of the community, trends and future challenges. With 145 authors from 19 distinct institutions, results show that a diversity of tasks, including software planning, requirements, design and testing, and a large variety of techniques has been used, from exact search to evolutionary computation and swarm intelligence. Further, since 2015, specific scientific events have helped to bring together the community, improving collaborations, financial funding and internationalisation.},
keywords = {Research trends, Search-based Software Engineering, Spanish community, Systematic review},
pubstate = {published},
tppubtype = {article}
}
Since its appearance in 2001, search-based software engineering has allowed software engineers to use optimisation techniques to automate distinctive human problems related to software management and development. The scientific community in Spain has not been alien to these advances. Their contributions cover both the optimisation of software engineering tasks and the proposal of new search algorithms. This review compiles the research efforts of this community in the area. With this aim, we propose a protocol to describe the review process, including the search sources, inclusion and exclusion criteria of candidate papers, the data extraction procedure and the categorisation of primary studies. After retrieving more than 3700 papers, 232 primary studies have been selected, whose analysis gives a precise picture of the current research state of the community, trends and future challenges. With 145 authors from 19 distinct institutions, results show that a diversity of tasks, including software planning, requirements, design and testing, and a large variety of techniques has been used, from exact search to evolutionary computation and swarm intelligence. Further, since 2015, specific scientific events have helped to bring together the community, improving collaborations, financial funding and internationalisation.
2018
Morales, Rodrigo; Saborido, Rubén; Khomh, Foutse; Chicano, Francisco; Antoniol, Giuliano
EARMO: An Energy-Aware Refactoring Approach for Mobile Apps Journal Article
In: IEEE Transactions on Software Engineering, vol. 44, no. 12, pp. 1176–1206, 2018, ISSN: 0098-5589.
Links | BibTeX | Tags: Androids, Anti-patterns, Batteries, Energy consumption, Energy measurement, Humanoid robots, Mobile apps, Mobile communication, Refactoring, Search-based Software Engineering, Software, Software maintenance
@article{morales_earmo:_2018,
title = {EARMO: An Energy-Aware Refactoring Approach for Mobile Apps},
author = {Rodrigo Morales and Rubén Saborido and Foutse Khomh and Francisco Chicano and Giuliano Antoniol},
doi = {10.1109/TSE.2017.2757486},
issn = {0098-5589},
year = {2018},
date = {2018-01-01},
journal = {IEEE Transactions on Software Engineering},
volume = {44},
number = {12},
pages = {1176--1206},
keywords = {Androids, Anti-patterns, Batteries, Energy consumption, Energy measurement, Humanoid robots, Mobile apps, Mobile communication, Refactoring, Search-based Software Engineering, Software, Software maintenance},
pubstate = {published},
tppubtype = {article}
}
2012
Ferrer, Javier; Chicano, Francisco; Alba, Enrique
Evolutionary algorithms for the multi-objective test data generation problem Journal Article
In: Software: Practice and Experience, vol. 42, no. 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.