Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/8537
Title: | Automated generation of computationally hard feature models using evolutionary algorithms |
Authors: | Parejo, JA Hierons, RM Benavides, D Ruiz-Cortés, A |
Keywords: | Search-based testing;Software product lines;Evolutionary algorithms;Feature models;Performance testing;Automated analysis |
Issue Date: | 2014 |
Publisher: | Elsevier |
Citation: | Expert Systems with Applications, 41(8), 3975 - 3992, 2014 |
Abstract: | A feature model is a compact representation of the products of a software product line. The automated extraction of information from feature models is a thriving topic involving numerous analysis operations, techniques and tools. Performance evaluations in this domain mainly rely on the use of random feature models. However, these only provide a rough idea of the behaviour of the tools with average problems and are not sufficient to reveal their real strengths and weaknesses. In this article, we propose to model the problem of finding computationally hard feature models as an optimization problem and we solve it using a novel evolutionary algorithm for optimized feature models (ETHOM). Given a tool and an analysis operation, ETHOM generates input models of a predefined size maximizing aspects such as the execution time or the memory consumption of the tool when performing the operation over the model. This allows users and developers to know the performance of tools in pessimistic cases providing a better idea of their real power and revealing performance bugs. Experiments using ETHOM on a number of analyses and tools have successfully identified models producing much longer executions times and higher memory consumption than those obtained with random models of identical or even larger size. |
Description: | This is the post-print version of the final paper published in Expert Systems with Applications. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2014 Elsevier B.V. |
URI: | http://www.sciencedirect.com/science/article/pii/S0957417413010038 http://bura.brunel.ac.uk/handle/2438/8537 |
DOI: | http://dx.doi.org/10.1016/j.eswa.2013.12.028 |
ISSN: | 0957-4174 |
Appears in Collections: | Publications Computer Science Dept of Computer Science Research Papers |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Fulltext.pdf | 427.46 kB | Adobe PDF | View/Open |
Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.