Scalable Sampling of Highly-Configurable Systems: Generating Random Instances of the Linux Kernel

Fernández Amoros, David, Heradio, Rubén, Mayr Dorn, Christoph y Egyed, Alexander(2023) .Scalable Sampling of Highly-Configurable Systems: Generating Random Instances of the Linux Kernel. ASE '22: 37th IEEE/ACM International Conference on Automated Software Engineering.En: Rochester (USA). (2022-10-10)

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Título de la Conferencia ASE '22: 37th IEEE/ACM International Conference on Automated Software Engineering
Fecha de inicio de la Conferencia 2022-10-10
Fecha fín de la Conferencia 2022-10-14
Lugar de la Conferencia Rochester (USA)
Fecha de presentación de la Ponencia 2022
Numeros de las páginas 1-12
Titulo Scalable Sampling of Highly-Configurable Systems: Generating Random Instances of the Linux Kernel
Autor(es) Fernández Amoros, David
Heradio, Rubén
Mayr Dorn, Christoph
Egyed, Alexander
Notas adicionales This is an Accepted Manuscript of an article published by ACM in "37th IEEE/ACM International Conference on Automated Software Engineering (ASE ’22), October 10–14, 2022, Rochester, USA, 12 pages.", available at: https://doi.org/10.1145/3551349.3556899 Este es el manuscrito aceptado del artículo publicado por ACM en "37th IEEE/ACM International Conference on Automated Software Engineering (ASE ’22), October 10–14, 2022, Rochester, USA, 12 pages." disponible en línea: https://doi.org/10.1145/3551349.3556899
Materia(s) Ingeniería Informática
Abstract Software systems are becoming increasingly configurable. A paradigmatic example is the Linux kernel, which can be adjusted for a tremendous variety of hardware devices, from mobile phones to supercomputers, thanks to the thousands of configurable features it supports. In principle, many relevant problems on configurable systems, such as completing a partial configuration to get the system instance that consumes the least energy or optimizes any other quality attribute, could be solved through exhaustive analysis of all configurations. However, configuration spaces are typically colossal and cannot be entirely computed in practice. Alternatively, configuration samples can be analyzed to approximate the answers. Generating those samples is not trivial since features usually have inter-dependencies that constrain the configuration space. Therefore, getting a single valid configuration by chance is extremely unlikely. As a result, advanced samplers are being proposed to generate random samples at a reasonable computational cost. However, to date, no sampler can deal with highly configurable complex systems, such as the Linux kernel. This paper proposes a new sampler that does scale for those systems, based on an original theoretical approach called extensible logic groups. The sampler is compared against five other approaches. Results show our tool to be the fastest and most scalable one.
Palabra clave random sampling
configurable systems
variability modeling
software product lines
SAT
binary decision diagrams
Kconfig
Editor(es) Association for Computing Machinery (ACM)
Fecha 2023-01-05
Formato application/pdf
Identificador bibliuned:DptoISSI-ETSI-Ponencias-Rheradio-0002
http://e-spacio.uned.es/fez/view/bibliuned:DptoISSI-ETSI-Ponencias-Rheradio-0002
https://doi.org/10.1145/3551349.3556899
Total de paginas 12
Versión de la publicación acceptedVersion
Nivel de acceso y licencia http://creativecommons.org/licenses/by-nc-nd/4.0
info:eu-repo/semantics/openAccess
Tipo de recurso conferenceObject
Tipo de acceso Acceso abierto

 
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Creado: Mon, 06 May 2024, 18:56:21 CET