skip to main content
10.1145/3236405.3236410acmotherconferencesArticle/Chapter ViewAbstractPublication PagessplcConference Proceedingsconference-collections
demonstration

KernelHaven: an open infrastructure for product line analysis

Published:10 September 2018Publication History

ABSTRACT

KernelHaven is an open infrastructure for Software Product Line (SPL) analysis. It is intended both as a production-quality analysis tool set as well as a research support tool, e.g., to support researchers in systematically exploring research hypothesis. For flexibility and ease of experimentation KernelHaven components are plug-ins for extracting certain information from SPL artifacts and processing this information, e.g., to check the correctness and consistency of variability information or to apply metrics. A configuration-based setup along with automatic documentation functionality allows different experiments and supports their easy reproduction.

Here, we describe KernelHaven as a product line analysis research tool and highlight its basic approach as well as its fundamental capabilities. In particular, we describe available information extraction and processing plug-ins and how to combine them. On this basis, researchers and interested professional users can rapidly conduct a first set of experiments. Further, we describe the concepts for extending KernelHaven by new plug-ins, which reduces development effort when realizing new experiments.

References

  1. Don Batory. 2005. Feature Models, Grammars, and Propositional Formulas. In 9th International Conference on Software Product Lines. Springer-Verlag, Berlin, Heidelberg, 7--20. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Thorsten Berger and Christian Kästner. 2016. KBuildMiner. (2016). https://github.com/ckaestne/KBuildMiner Last visited: 18.04.2018.Google ScholarGoogle Scholar
  3. Carl Boettiger. 2015. An Introduction to Docker for Reproducible Research. Operating Systems Review - Special Issue on Repeatability and Sharing of Experimental Artifacts 49, 1 (2015), 71--79. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Jürgen Cito and Harald C. Gall. 2016. Using Docker Containers to Improve Reproducibility in Software Engineering Research. In 38th International Conference on Software Engineering Companion. ACM, New York, NY, USA, 906--907. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. srcML Community. 2018. srcML. (2018). http://www.srcml.org/ Last visited: 18.04.2018.Google ScholarGoogle Scholar
  6. Christian Dietrich, Reinhard Tartler, Wolfgang Schröder-Preikschat, and Daniel Lohmann. 2012. A Robust Approach for Variability Extraction from the Linux Build System. In 16th International Software Product Line Conference, Vol. 1. ACM, New York, NY, USA, 21--30. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Holger Eichelberger, Aike Sass, and Klaus Schmid. 2016. From Reproducibility Problems to Improvements: A journey. Softwaretechnik-Trends 36, 4 (2016), 43--45.Google ScholarGoogle Scholar
  8. Sascha El-Sharkawy, Saura Jyoti Dhar, Adam Krafczyk, Slawomir Duszynski, Tobias Beichter, and Klaus Schmid. 2018. Reverse Engineering Variability in an Industrial Product Line: Observations and Lessons Learned. In Proceedings of the 22nd International Systems and Software Product Line Conference (SPLC '18). ACM, New York, NY, USA, 11. accepted. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Sascha El-Sharkawy, Adam Krafczyk, and Klaus Schmid. 2017. An Empirical Study of Configuration Mismatches in Linux. In 21st International Systems and Software Product Line Conference, Vol. A. ACM, New York, NY, USA, 19--28. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Sascha El-Sharkawy, Nozomi Yamagishi-Eichler, and Klaus Schmid. 2018. Implementation Metrics for Software Product Lines - A Systematic Literature Review. Technical Report 1/2018, SSE 1/18/E. Institute of Computer Science, University of Hildesheim. Available at https://sse.uni-hildesheim.de/en/research/projects/revamp2/spl-metrics/.Google ScholarGoogle Scholar
  11. Christian Kästner. 2013. Type Chef. (2013). https://ckaestne.github.io/TypeChef/Last visited: 18.14.2018.Google ScholarGoogle Scholar
  12. Christian Kästner. 2016. KConfigReader. (2016). https://github.com/ckaestne/kconfgreader Last visited: 18.04.2018.Google ScholarGoogle Scholar
  13. KBuild. 2018. KBuild - The Linux Kernel Build System. (2018). https://www.kernel.org/doc/Documentation/kbuild/ Last visited: 18.04.2018.Google ScholarGoogle Scholar
  14. Christian Kröher, Sascha El-Sharkawy, and Klaus Schmid. 2018. KernelHaven - An Experimentation Workbench for Analyzing Software Product Lines. In 40th International Conference on Software Engineering: Companion Proceedings. ACM, New York, NY, USA, 73--76. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Sarah Nadi, Thorsten Berger, Christian Kästner, and Krzysztof Czarnecki. 2015. Where do Configuration Constraints Stem From? An Extraction Approach and an Empirical Study. IEEE Transactions on Software Engineering 41, 8 (2015), 820--841.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Sarah Nadi and Ric Holt. 2012. Mining Kbuild to detect variability anomalies in Linux. In 16th European Conference on Software Maintenance and Reengineering. IEEE Computer Society, Washington, DC, USA, 107--116. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Reinhard Tartler, Daniel Lohmann, Julio Sincero, and Wolfgang Schröder-Preikschat. 2011. Feature Consistency in Compile-time-configurable System Software: Facing the Linux 10,000 Feature Problem. In 6th Conference on Computer Systems. ACM, New York, NY, USA, 47--60. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. VAMOS/CADOS Team. 2015. Undertaker. (2015). https://vamos.informatik.uni-erlangen.de/trac/undertaker Last visited: 18.04.2018.Google ScholarGoogle Scholar

Index Terms

  1. KernelHaven: an open infrastructure for product line analysis

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image ACM Other conferences
          SPLC '18: Proceedings of the 22nd International Systems and Software Product Line Conference - Volume 2
          September 2018
          101 pages
          ISBN:9781450359450
          DOI:10.1145/3236405

          Copyright © 2018 Owner/Author

          Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 10 September 2018

          Check for updates

          Qualifiers

          • demonstration

          Acceptance Rates

          Overall Acceptance Rate167of463submissions,36%

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader