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ConfigCrusher: towards white-box performance analysis for configurable systems

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Abstract

Stakeholders of configurable systems are often interested in knowing how configuration options influence the performance of a system to facilitate, for example, the debugging and optimization processes of these systems. Several black-box approaches can be used to obtain this information, but they either sample a large number of configurations to make accurate predictions or miss important performance-influencing interactions when sampling few configurations. Furthermore, black-box approaches cannot pinpoint the parts of a system that are responsible for performance differences among configurations. This article proposes ConfigCrusher, a white-box performance analysis that inspects the implementation of a system to guide the performance analysis, exploiting several insights of configurable systems in the process. ConfigCrusher employs a static data-flow analysis to identify how configuration options may influence control-flow statements and instruments code regions, corresponding to these statements, to dynamically analyze the influence of configuration options on the regions’ performance. Our evaluation on 10 configurable systems shows the feasibility of our white-box approach to more efficiently build performance-influence models that are similar to or more accurate than current state of the art approaches. Overall, we showcase the benefits of white-box performance analyses and their potential to outperform black-box approaches and provide additional information for analyzing configurable systems.

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Notes

  1. Though SPLat was designed for unit testing software product lines, the algorithm can be used to reduce the number of configurations to sample.

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Acknowledgements

This work has been supported in part by the NSF (Awards 1318808, 1552944, 1717022), AFRL, DARPA (FA8750-16-2-0042), and the German Research Foundation (AP 206/7-2, AP 206/11-1, SI 2171/2, SI 2171/3-1). We thank Chu-Pan Wong and Jens Meinicke for their comments during the development of this work. We thank the FOSD 2017 and 2018 meeting participants for their feedback on the central idea of this work. We thank Steven Artz for his help with FlowDroid.

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  1. Carnegie Mellon University, Pittsburgh, USA

    Miguel Velez & Christian Kästner

  2. University of South Carolina, Columbia, USA

    Pooyan Jamshidi

  3. Saarland University, Saarland Informatics Campus, Saarbrücken, Germany

    Florian Sattler & Sven Apel

  4. Leipzig University, Leipzig, Germany

    Norbert Siegmund

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  1. Miguel Velez
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Velez, M., Jamshidi, P., Sattler, F. et al. ConfigCrusher: towards white-box performance analysis for configurable systems. Autom Softw Eng 27, 265–300 (2020). https://doi.org/10.1007/s10515-020-00273-8

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