Anomaly detection in Context-aware Feature Models

Jacopo Mauro*

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Publikation: Kapitel i bog/rapport/konference-proceedingKonferencebidrag i proceedingsForskningpeer review

Abstrakt

Feature Models are a mechanism to organize the configuration space and facilitate the construction of software variants by describing configuration options using features, i.e., a name representing a functionality. The development of Feature Models is an error prone activity and detecting their anomalies is a challenging and important task needed to promote their usage. Feature Models have been extended with context to capture the correlation of configuration options with contextual influences and user customizations. Unfortunately, this extension makes the task of detecting anomalies harder. In this paper, we formalize the anomaly analysis in Context-aware Feature Models and we show how Quantified Boolean Formula (QBF) solvers can be used to detect anomalies without relying on iterative calls to a SAT solver. By extending the reconfigurator engine HyVarRec, we present findings evidencing that QBF solvers can outperform the common techniques for anomaly analysis on some instances.

OriginalsprogEngelsk
TitelProceedings - VaMoS 2021 : 15th International Working Conference on Variability Modelling of Software-Intensive Systems
RedaktørerPaul Grunbacher
Antal sider9
ForlagAssociation for Computing Machinery
Publikationsdato9. feb. 2021
Artikelnummer3442405
ISBN (Elektronisk)9781450388245
DOI
StatusUdgivet - 9. feb. 2021
Begivenhed15th International Working Conference on Variability Modelling of Software-Intensive Systems, VaMoS 2021 - Virtual, Online, Østrig
Varighed: 9. feb. 202111. feb. 2021

Konference

Konference15th International Working Conference on Variability Modelling of Software-Intensive Systems, VaMoS 2021
Land/OmrådeØstrig
ByVirtual, Online
Periode09/02/202111/02/2021

Bibliografisk note

Publisher Copyright:
© 2021 ACM.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

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