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Tackling combinatorial explosion: A study of industrial needs and practices for analyzing highly configurable systems

Paper i proceeding
Författare Mukelabai Mukelabai
Damir Nešić
Salome Maro
Thorsten Berger
Jan-Philipp Steghöfer
Publicerad i ASE 2018 - Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering
Publiceringsår 2018
Publicerad vid Institutionen för data- och informationsteknik (GU)
Institutionen för data- och informationsteknik, datavetenskap (GU)
Språk en
Ämnesord Analysis, Highly configurable systems, Software product lines
Ämneskategorier Programvaruteknik

Sammanfattning

© 2018 Association for Computing Machinery. 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 followup 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.

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