Hyper-Heuristic Based Product Selection for Software Product Line Testing
Published in IEEE Computational Intelligence Magazine, 2017
Recommended citation: T. N. Ferreira, J. A. Prado Lima, A. Strickler, J. N. Kuk, S. R. Vergilio, and A. Pozo. 2017. Hyper-heuristic Based Product Selection for Software Product Line Testing. IEEE Computational Intelligence Magazine 12, 2 (May 2017), 34–45. https://ieeexplore.ieee.org/document/7895294/
DOI: 10.1109/MCI.2017.2670461
Abstract
A Software Product Line (SPL) is defined as a set of software systems that share a common and managed set of features satisfying specific needs of a particular market segment or domain [1]. The SPL offers a number of common artifacts for building products, including mandatory and variable elements. SPL approaches have been adopted by many software companies1 to ease reuse and reduce time and production costs. A feature represents a functionality that is visible to the user and can be designed as a variability, which represents a variable functionality that may or may not be present in a product. On the other hand, mandatory features are common to all SPL products. To facilitate feature management, most SPL methodologies use the Feature Model (FM) [2] to represent all the SPL variabilities and commonalities.
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Recommended citation: T. N. Ferreira, J. A. Prado Lima, A. Strickler, J. N. Kuk, S. R. Vergilio, and A. Pozo. 2017. Hyper-heuristic Based Product Selection for Software Product Line Testing. IEEE Computational Intelligence Magazine 12, 2 (May 2017), 34–45.