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ConfigCrusher: towards white-box performance analysis for configurable systems
Automated Software Engineering ( IF 3.4 ) Pub Date : 2020-08-05 , DOI: 10.1007/s10515-020-00273-8
Miguel Velez , Pooyan Jamshidi , Florian Sattler , Norbert Siegmund , Sven Apel , Christian Kästner

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.

中文翻译:

ConfigCrusher:面向可配置系统的白盒性能分析

可配置系统的利益相关者通常对了解配置选项如何影响系统性能感兴趣,例如,以促进这些系统的调试和优化过程。可以使用几种黑盒方法来获取此信息,但它们要么采样大量配置以进行准确预测,要么在采样少量配置时错过影响性能的重要交互。此外,黑盒方法无法精确定位导致配置之间性能差异的系统部分。本文提出了 ConfigCrusher,这是一种白盒性能分析,它检查系统的实现以指导性能分析,在此过程中利用了可配置系统的几个见解。ConfigCrusher 采用静态数据流分析来确定配置选项如何影响控制流语句并检测与这些语句对应的代码区域,以动态分析配置选项对区域性能的影响。我们对 10 个可配置系统的评估表明,我们的白盒方法可以更有效地构建与当前最先进方法相似或更准确的性能影响模型。总的来说,我们展示了白盒性能分析的优势及其优于黑盒方法的潜力,并为分析可配置系统提供了额外的信息。动态分析配置选项对区域性能的影响。我们对 10 个可配置系统的评估表明,我们的白盒方法可以更有效地构建与当前最先进方法相似或更准确的性能影响模型。总的来说,我们展示了白盒性能分析的优势及其优于黑盒方法的潜力,并为分析可配置系统提供了额外的信息。动态分析配置选项对区域性能的影响。我们对 10 个可配置系统的评估表明,我们的白盒方法可以更有效地构建与当前最先进方法相似或更准确的性能影响模型。总的来说,我们展示了白盒性能分析的优势及其优于黑盒方法的潜力,并为分析可配置系统提供了额外的信息。
更新日期:2020-08-05
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