kconfig-webconf: Retrofitting Performance Models onto Kconfig-Based Software Product Lines

Autor(en): Friesel, D.
Elmenhorst, K.
Kaiser, L.
Müller, M.
Spinczyk, O. 
Herausgeber: Felfernig, A.
Fuentes, L.
Cleland-Huang, J.
Assuncao, W.K.G.
Quinton, C.
Guo, J.
Schmid, K.
Huchard, M.
Ayala, I.
Rojas, J.M.
Le, V.-M.
Horcas, J.M.
Stichwörter: kconfig; Non functional properties; Open systems; Performance; Performance Modeling; performance prediction; product lines; Productline; Real-world; regression trees; Software design, Configuration software; Software Product Line, Open source software
Erscheinungsdatum: 2022
Herausgeber: Association for Computing Machinery, Inc
Journal: 26th ACM International Systems and Software Product Line Conference, SPLC 2022 - Proceedings
Volumen: B
Startseite: 58
Seitenende: 61
Zusammenfassung: 
Despite decades of research and clear advantages, performance-aware configuration of real-world software product lines is still an exception rather than the norm. One reason for this may be tooling: configuration software with support for non-functional property models is generally not compatible with the configuration and build process of existing product lines. Specifically, the Kconfig language is popular in open source software projects, but neither language nor configuration frontends support performance models. To address this, we present kconfig-webconf: a performance-aware, Kconfig-compatible software product line configuration frontend. It is part of a toolchain that can automatically generate performance models with a minimal amount of changes to a software product line's build process. With such a performance model, kconfig-webconf can serve as a performance-aware drop-in replacement for existing Kconfig frontends. We evaluate its usage in five examples, including the busybox multi-call binary and the resKIL agricultural AI product line. © 2022 ACM.
Beschreibung: 
Conference of 26th ACM International Systems and Software Product Line Conference, ASPLC 2022 ; Conference Date: 12 September 2022 Through 16 September 2022; Conference Code:182715
ISBN: 9781450392068
DOI: 10.1145/3503229.3547026
Externe URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139174875&doi=10.1145%2f3503229.3547026&partnerID=40&md5=9efeb375b2c3fd513f283b707f27112f

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