Anomaly detection in Context-aware Feature Models

Jacopo Mauro*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - VaMoS 2021 : 15th International Working Conference on Variability Modelling of Software-Intensive Systems
EditorsPaul Grunbacher
Number of pages9
PublisherAssociation for Computing Machinery
Publication date9. Feb 2021
Article number3442405
ISBN (Electronic)9781450388245
DOIs
Publication statusPublished - 9. Feb 2021
Event15th International Working Conference on Variability Modelling of Software-Intensive Systems, VaMoS 2021 - Virtual, Online, Austria
Duration: 9. Feb 202111. Feb 2021

Conference

Conference15th International Working Conference on Variability Modelling of Software-Intensive Systems, VaMoS 2021
CountryAustria
CityVirtual, Online
Period09/02/202111/02/2021

Keywords

  • Feature Model Anomalies
  • SMT solver

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