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Tackling combinatorial explosion: a study of industrial needs and practices for analyzing highly configurable systems
Chalmers University of Technology / University of Gothenburg.
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
Chalmers University of Technology / University of Gothenburg.
Chalmers University of Technology / University of Gothenburg.
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2018 (English)In: Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering, 2018, p. 155-166Conference paper, Published paper (Refereed)
Abstract [en]

Highly configurable systems are complex pieces of software. To tackle this complexity, hundreds of dedicated analysis techniques have been conceived, many of which able to analyze system properties for all possible system configurations, as opposed to traditional, single-system analyses. Unfortunately, it is largely unknown whether these techniques are adopted in practice, whether they address actual needs, or what strategies practitioners actually apply to analyze highly configurable systems. We present a study of analysis practices and needs in industry. It relied on a survey with 27 practitioners engineering highly configurable systems and follow-up interviews with 15 of them, covering 18 different companies from eight countries. We confirm that typical properties considered in the literature (e.g., reliability) are relevant, that consistency between variability models and artifacts is critical, but that the majority of analyses for specifications of configuration options (a.k.a., variability model analysis) is not perceived as needed. We identified rather pragmatic analysis strategies, including practices to avoid the need for analysis. For instance, testing with experience-based sampling is the most commonly applied strategy, while systematic sampling is rarely applicable. We discuss analyses that are missing and synthesize our insights into suggestions for future research.

Place, publisher, year, edition, pages
2018. p. 155-166
National Category
Computer Systems
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-233846DOI: 10.1145/3238147.3238201Scopus ID: 2-s2.0-85056534298ISBN: 978-1-4503-5937-5 (print)OAI: oai:DiVA.org:kth-233846DiVA, id: diva2:1243004
Conference
International Conference on Automated Software Engineering
Note

QC 20180903

Available from: 2018-08-29 Created: 2018-08-29 Last updated: 2019-03-18Bibliographically approved

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CiteExportLink to record
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Citation style
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