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Resultados de búsqueda para Evolutionary algorithms

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

Fragment retrieval on models for model maintenance: Applying a multi-objective perspective to an industrial case study

Context: Traceability Links Recovery (TLR), Bug Localization (BL), and Feature Location (FL) are amongst the most relevant tasks performed during software maintenance. However, most research in the field targets code, while models have not received enough attention yet.Objective: This paper presents our approach (FROM, Fragment Retrieval on Models) that uses an Evolutionary Algorithm to retrieve the most relevant model fragments for three different types of input queries: natural language requirements for TLR, bug descriptions for BL, and feature descriptions for FL.Method: FROM uses an Evolutionary Algorithm that generates model fragments through genetic operations, and assesses the relevance of each model fragment with regard to the provided query through a fitness configuration. We analyze the influence that four fitness configurations have over the results of FROM, combining three objectives: Similitude, Understandability, and Timing. To analyze this, we use a real-world case study from our industrial partner, which is a worldwide leader in train manufacturing. We record the results in terms of recall, precision, and F-measure. Moreover, results are compared against those obtained by a baseline, and a statistical analysis is performed to provide evidences of the significance of the results.Results: The results show that FROM can be applied in our industrial case study. Also, the results show that the configurations and the baseline have significant differences in performance for TLR, BL, and FL tasks. Moreover, our results show that there is no single configuration that is powerful enough to obtain the best results in all tasks.Conclusions: The type of task performed (TLR, BL, and FL) during the retrieval of model fragments has an actual impact on the results of the configurations of the Evolutionary Algorithm. Our findings suggest which configuration offers better results as well as the objectives that do not contribute to improve the results.

Autores: Francisca Pérez / Raúl Lapeña / Jaime Font / Carlos Cetina / 
Palabras Clave: Bug localization - Conceptual models - Evolutionary algorithms - Feature location - Traceability links recovery

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