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SPL Conqueror: Toward optimization of non-functional properties in software product lines

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Abstract

A software product line (SPL) is a family of related programs of a domain. The programs of an SPL are distinguished in terms of features, which are end-user visible characteristics of programs. Based on a selection of features, stakeholders can derive tailor-made programs that satisfy functional requirements. Besides functional requirements, different application scenarios raise the need for optimizing non-functional properties of a variant. The diversity of application scenarios leads to heterogeneous optimization goals with respect to non-functional properties (e.g., performance vs. footprint vs. energy optimized variants). Hence, an SPL has to satisfy different and sometimes contradicting requirements regarding non-functional properties. Usually, the actually required non-functional properties are not known before product derivation and can vary for each application scenario and customer. Allowing stakeholders to derive optimized variants requires us to measure non-functional properties after the SPL is developed. Unfortunately, the high variability provided by SPLs complicates measurement and optimization of non-functional properties due to a large variant space. With SPL Conqueror, we provide a holistic approach to optimize non-functional properties in SPL engineering. We show how non-functional properties can be qualitatively specified and quantitatively measured in the context of SPLs. Furthermore, we discuss the variant-derivation process in SPL Conqueror that reduces the effort of computing an optimal variant. We demonstrate the applicability of our approach by means of nine case studies of a broad range of application domains (e.g., database management and operating systems). Moreover, we show that SPL Conqueror is implementation and language independent by using SPLs that are implemented with different mechanisms, such as conditional compilation and feature-oriented programming.

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Notes

  1. Please note, a feature model looks similar to a goal model often used for requirements engineering (van Lamsweerde 2001). However, the concepts cannot be compared. A feature model describes the variability of an entire SPL, i.e., all products.

  2. Available at: http://www.oracle.com/technetwork/database/berkeleydb/.

  3. The sum of the footprint of features Base, Replication and Cryptography is 448 KB.

  4. Available at: http://sqlite.org.

  5. In fact, a single measurement takes approximately 5 min. Measuring all 288 variants would take ca. 2.9 × 1021 years.

  6. Measurement theory defines which operations are valid for which scale of measurement. For example, we can only use median and percentile operations for an ordinal scale, because we only have a totally ordered set of measures.

  7. Maintainability can be derived from source code metrics to some degree.

  8. We provide the raw material of our measurements and evaluations on our website: http://fosd.de/SPLConqueror.

  9. http://www.campwoodsw.com/sourcemonitor.html.

  10. The SPLs are: LinkedList, ZipMe, PKJab, SensorNetwork and Violet.

  11. Please note, that this is only an example. Such a design decision should be made by domain experts and SPL vendors.

  12. The complete measurement can be found at our website: http://fosd.de/SPLConqueror.

  13. Since feature Compress is a mandatory feature, it is present in every product. Hence, the size of this feature is measured together with the size of the SPL’s core feature Base.

  14. We used the Microsoft C compiler and /O2 optimization level as compiler flag.

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Acknowledgment

We would like to thank Janet Feigenspan and the anonymous reviewers for their constructive and helpful comments which substantially improved the quality of the paper. Norbert Siegmund is funded by the German ministry of education and science BMBF, number 01IM10002B. Marko Rosenmüller is funded by the German research foundation, project number SA 465/34-1. Apel’s work is supported by the DFG projects #AP 206/2-1 and #AP 206/4-1. Kästner’s work is supported by the European Research Council (grand ScalPL #203099).

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  1. University of Magdeburg, Magdeburg, Germany

    Norbert Siegmund, Marko Rosenmüller, Martin Kuhlemann & Gunter Saake

  2. Philipps University Marburg, Marburg, Germany

    Christian Kästner

  3. University of Passau, Passau, Germany

    Sven Apel

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  1. Norbert Siegmund
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Correspondence to Norbert Siegmund.

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Siegmund, N., Rosenmüller, M., Kuhlemann, M. et al. SPL Conqueror: Toward optimization of non-functional properties in software product lines. Software Qual J 20, 487–517 (2012). https://doi.org/10.1007/s11219-011-9152-9

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