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Using Ant Colony Optimisation to Select Features Having Associated Costs

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Testing Software and Systems (ICTSS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13045))

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Abstract

Software Product Lines (SPLs) strongly facilitate the automation of software development processes. They combine features to create programs (called products) that fulfil certain properties. Testing SPLs is an intensive process where choosing the proper products to include in the testing process can be a critical task. In fact, selecting the best combination of features from an SPL is a complex problem that is frequently addressed in the literature. In this paper we use evolutionary algorithms to find a combination of features with low testing cost that include a target feature, to facilitate the integration testing of such feature. Specifically, we use an Ant Colony Optimisation algorithm to find one of the cheapest (in terms of testing) combination of features that contains a specific feature. Our results show that our framework overcomes the limitations of both brute force and random search algorithms.

This work has been supported by the Spanish MINECO/FEDER project FAME (RTI2018-093608-B-C31); the Region of Madrid project FORTE-CM (S2018/TCS-4314) co-funded by EIE Funds of the European Union; and the Santander - Complutense University of Madrid (grant number CT63/19-CT64/19).

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Acknowledgements

We would like to thank the anonymous reviewers for the careful reading, the many constructive comments and the useful suggestions, which have helped us to further strengthen the paper.

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Authors and Affiliations

  1. Universidad Complutense de Madrid, Madrid, 28040, Spain

    Alfredo Ibias, Luis Llana & Manuel Núñez

Authors
  1. Alfredo Ibias
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  2. Luis Llana
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  3. Manuel Núñez
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Corresponding author

Correspondence to Alfredo Ibias .

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Editors and Affiliations

  1. University College London, London, UK

    David Clark

  2. Middlesex University, London, UK

    Hector Menendez

  3. Telecom SudParis, Evry Cedex, France

    Ana Rosa Cavalli

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Ibias, A., Llana, L., Núñez, M. (2022). Using Ant Colony Optimisation to Select Features Having Associated Costs. In: Clark, D., Menendez, H., Cavalli, A.R. (eds) Testing Software and Systems. ICTSS 2021. Lecture Notes in Computer Science, vol 13045. Springer, Cham. https://doi.org/10.1007/978-3-031-04673-5_8

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  • DOI: https://doi.org/10.1007/978-3-031-04673-5_8

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  • Online ISBN: 978-3-031-04673-5

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