Towards scalable configuration testing in variable software

Rothberg, Valentin; Dietrich, Christian; Ziegler, Andreas; Lohmann, Daniel

doi:10.1145/2993236.2993252

Cited by 2 publications

(4 citation statements)

References 26 publications

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“…So we had to stick with randconfig. We see randconfig as a baseline widely used by the Linux community [47,59]. The computation of feature importance is subject to some debates and some implementation over random forest, including the scikit-learn's one we rely on, may be biased [8,18,48,54].…”

Section: Threats To Validitymentioning

confidence: 99%

Feature subset selection for learning huge configuration spaces

Acher¹,

Martin²,

Pereira³

et al. 2022

Proceedings of the 26th ACM International Systems and Software Product Line Conference - Volume A

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Linux kernels are used in a wide variety of appliances, many of them having strong requirements on the kernel size due to constraints such as limited memory or instant boot. With more than nine thousands of configuration options to choose from, developers and users of Linux actually spend significant effort to document, understand, and eventually tune (combinations of) options for meeting a kernel size. In this paper, we describe a large-scale endeavour automating this task and predicting a given Linux kernel binary size out of unmeasured configurations. We first experiment that state-of-theart solutions specifically made for configurable systems such as performance-influence models cannot cope with that number of options, suggesting that software product line techniques may need to be adapted to such huge configuration spaces. We then show that tree-based feature selection can learn a model achieving low prediction errors over a reduced set of options. The resulting model, trained on 95 854 kernel configurations, is fast to compute, simple to interpret and even outperforms the accuracy of learning without feature selection.

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Section: Threats To Validitymentioning

confidence: 99%

Feature subset selection for learning huge configuration spaces

Acher¹,

Martin²,

Pereira³

et al. 2022

Proceedings of the 26th ACM International Systems and Software Product Line Conference - Volume A

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show abstract

“…The Linux kernel networking documentation, for example, explicitly suggests that all submitted changes should be first tested with the allyesconfig and allmodconfig configurations. 4 JMake currently focuses on allyesconfig. The various architectures supported by the Linux kernel also provide a selection of sample configurations in the configs subdirectory of the architecture support code.…”

Section: B Linux Kernel Configurationmentioning

confidence: 99%

“…The resulting configuration, however, is forced to make some choices and thus does not include all lines of code. Vampyr [5] and Troll [4], provide strategies to construct a set of configurations that will maximize the coverage of a static analysis tool, such as a compiler, on a changed file. Sampling strategies have also been used for this purpose; Medeiros et al [9] review 10 such strategies and compare their efficiency in fault finding.…”

Section: Related Workmentioning

confidence: 99%

“…Even worse, some lines within a file may be included only by specific configurations, giving the janitor a false sense of security when the file compiles but some of the changed lines have not actually been subjected to the compiler. While Intel has put in place a "0-day build testing service" [3] that compiles all patches for a number of randomly selected configurations and reports back to the patch submitter on any issue, these tests are also not exhaustive [4] and the timing of the feedback is not under the control of the patch submitter.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

JMake: Dependable Compilation for Kernel Janitors

Lawall¹,

Muller²

2017

2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)

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The Linux kernel is highly configurable, and thus, in principle, any line of code can be included or excluded from the compiled kernel based on configuration operations. Configurability complicates the task of a kernel janitor, who cleans up faults across the code base. A janitor may not be familiar with the configuration options that trigger compilation of a particular code line, leading him to believe that a fix has been compile-checked when this is not the case. We propose JMake, a mutation-based tool for signaling changed lines that are not subjected to the compiler. JMake shows that for most of the 12,000 file-modifying commits between Linux v4.3 and v4.4 the configuration chosen by the kernel allyesconfig option is sufficient, once the janitor chooses the correct architecture. For most commits, this check requires only 30 seconds or less. We then characterize the situations in which changed code is not subjected to compilation in practice.

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Towards scalable configuration testing in variable software

Cited by 2 publications

References 26 publications

Feature subset selection for learning huge configuration spaces

Feature subset selection for learning huge configuration spaces

JMake: Dependable Compilation for Kernel Janitors

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