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El autor Rob Hierons ha publicado 2 artículo(s):

1 - SIP: Optimal Product Selection from Feature Models Using Many-Objective Evolutionary Optimization

Robert M. Hierons, Miqing Li, Xiaohui Liu, Sergio Segura, and Wei Zheng. 2016. SIP: Optimal Product Selection from Feature Models Using Many-Objective Evolutionary Optimization. ACM Trans. Softw. Eng. Methodol. 25, 2, Article 17 (April 2016), 39 pages. DOI: http://dx.doi.org/10.1145/2897760 Indicadores de calidad: – Revista de referencia en el área de Ingeniería del Software (CS-SE: 21/106). – Colaboración internacional con los profesores Robert Hierons [1] y XiaoHui Liu [2]. – Hemos sido invitados a presentar el trabajo en FSE16 e ICSE17 como parte de la iniciativa journal-first (ver programa de la conferencia [3]). – Ha recibido 6 citas desde su publicación en abril de 2016 [4]. [1] http://dblp.uni-trier.de/pers/hd/h/Hierons:Robert_M= [2] http://dblp.uni-trier.de/pers/hd/l/Liu:Xiaohui [2] http://icse2017.gatech.edu/?q=technical-research-accepted [4] https://goo.gl/XyTmQR

Autores: Rob Hierons / Miqing Li / Xiaohui Liu Liu / Sergio Segura / Wei Zheng / 
Palabras Clave: Optimization - Search-Based Software Engineering - software product lines

2 - Many-Objective Test Suite Generation for Software Product Lines

A Software Product Line (SPL) is a set of products builtfrom a number of features, the set of valid products being dened bya feature model. Typically, it does not make sense to test all productsdened by an SPL and one instead chooses a set of products to test(test selection) and, ideally, derives a good order in which to test them(test prioritisation). Since one cannot know in advance which productswill reveal faults, test selection and prioritisation are normally based onobjective functions that are known to relate to likely effectiveness orcost. This article introduces a new technique, the grid-based evolutionstrategy (GrES), which considers several objective functions that assessa selection or prioritisation and aims to optimise on all of these. Theproblem is thus a many-objective optimisation problem. We use a newapproach, in which all of the objective functions are considered but one(pairwise coverage) is seen as the most important. We also derive a novelevolution strategy based on domain knowledge. The results of the evalua-tion, on randomly generated and realistic feature models, were promising,with GrES outperforming previously proposed techniques and a range ofmany-objective optimisation algorithms.

Autores: Rob Hierons / Miqing Li / Xiaohui Liu / José Antonio Parejo Maestre / Sergio Segura Rueda / Xin Yao / 
Palabras Clave: Evolutionary algorithms - many-objectives optimization - Search-Based Software Engineering - software product lines - Testing