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A preliminary empirical assessment of similarity for combinatorial interaction testing of software product lines

Published: 14 May 2016 Publication History

Abstract

Extensive work on Search-Based Software Testing for Software Product Lines has been published in the last few years. Salient among them is the use of similarity as a surrogate metric for t-wise coverage whenever higher strengths are needed or whenever the size of the test suites is infeasible because of technological or budget limitations. Though promising, this metric has not been assessed with real fault data. In this paper, we address this limitation by using Drupal, a widely used open source web content management system, as an industry-strength case study for which both variability information and fault data have been recently made available. Our preliminary assessment corroborates some of the previous findings but also raises issues on some assumptions and claims made. We hope our work encourages further empirical evaluations of Combinatorial Interaction Testing approaches for Software Product Lines.

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  • (2023)A Monte Carlo tree search conceptual framework for feature model analysesJournal of Systems and Software10.1016/j.jss.2022.111551195:COnline publication date: 1-Jan-2023
  • (2022)Looking For Novelty in Search-Based Software Product Line TestingIEEE Transactions on Software Engineering10.1109/TSE.2021.305785348:7(2317-2338)Online publication date: 1-Jul-2022
  • (2020)Measuring effectiveness of sample-based product-line testingACM SIGPLAN Notices10.1145/3393934.327813053:9(119-133)Online publication date: 7-Apr-2020
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  1. A preliminary empirical assessment of similarity for combinatorial interaction testing of software product lines

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    cover image ACM Conferences
    SBST '16: Proceedings of the 9th International Workshop on Search-Based Software Testing
    May 2016
    68 pages
    ISBN:9781450341660
    DOI:10.1145/2897010
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 14 May 2016

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    View all
    • (2023)A Monte Carlo tree search conceptual framework for feature model analysesJournal of Systems and Software10.1016/j.jss.2022.111551195:COnline publication date: 1-Jan-2023
    • (2022)Looking For Novelty in Search-Based Software Product Line TestingIEEE Transactions on Software Engineering10.1109/TSE.2021.305785348:7(2317-2338)Online publication date: 1-Jul-2022
    • (2020)Measuring effectiveness of sample-based product-line testingACM SIGPLAN Notices10.1145/3393934.327813053:9(119-133)Online publication date: 7-Apr-2020
    • (2018)Measuring effectiveness of sample-based product-line testingProceedings of the 17th ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences10.1145/3278122.3278130(119-133)Online publication date: 5-Nov-2018
    • (2018)Similarity analysis of product-line variantsProceedings of the 22nd International Systems and Software Product Line Conference - Volume 110.1145/3233027.3233044(226-235)Online publication date: 10-Sep-2018
    • (2018)A classification of product sampling for software product linesProceedings of the 22nd International Systems and Software Product Line Conference - Volume 110.1145/3233027.3233035(1-13)Online publication date: 10-Sep-2018
    • (2018)Propagating configuration decisions with modal implication graphsProceedings of the 40th International Conference on Software Engineering10.1145/3180155.3180159(898-909)Online publication date: 27-May-2018
    • (2018)Genetic Algorithm-based Test Generation for Software Product Line with the Integration of Fault Localization TechniquesEmpirical Software Engineering10.1007/s10664-016-9494-923:1(1-51)Online publication date: 1-Feb-2018

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