Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/7574
Title: Automated test data generation on the analyses of feature models: A metamorphic testing approach
Authors: Segura, S
Benavides, D
Ruiz-Cortés, A
Hierons, RM
Issue Date: 2010
Publisher: IEEE
Citation: ICST 2010 - 3rd International Conference on Software Testing, Verification and Validation, pp. 35 - 44, Apr 2010
Abstract: A Feature Model (FM) is a compact representation of all the products of a software product line. The automated extraction of information from FMs is a thriving research topic involving a number of analysis operations, algorithms, paradigms and tools. Implementing these operations is far from trivial and easily leads to errors and defects in analysis solutions. Current testing methods in this context mainly rely on the ability of the tester to decide whether the output of an analysis is correct. However, this is acknowledged to be time-consuming, error-prone and in most cases infeasible due to the combinatorial complexity of the analyses. In this paper, we present a set of relations (so-called metamorphic relations) between input FMs and their set of products and a test data generator relying on them. Given an FM and its known set of products, a set of neighbour FMs together with their corresponding set of products are automatically generated and used for testing different analyses. Complex FMs representing millions of products can be efficiently created applying this process iteratively. The evaluation of our approach using mutation testing as well as real faults and tools reveals that most faults can be automatically detected within a few seconds.
Description: This is the post-print version of the Article. The official published version can be accessed from the links below. Copyright © 2010 IEEE.
URI: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5477103
http://bura.brunel.ac.uk/handle/2438/7574
DOI: http://dx.doi.org/10.1109/ICST.2010.20
ISBN: 978-1-4244-6435-7
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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