Anomaly detection and explanation in context-aware software product lines

Jacopo Mauro, Michael Nieke, Christoph Seidl, Ingrid Chieh Yu

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

Abstract

A software product line (SPL) uses a variability model, such as a feature model (FM), to describe the configuration options for a set of closely related software systems. Context-aware SPLs also consider possible environment conditions for their configuration options. Errors in modeling the FM and its context may lead to anomalies, such as dead features or a void feature model, which reduce if not negate the usefulness of the SPL. Detecting these anomalies is usually done by using Boolean satisfiability (SAT) that however are not expressive enough to detect anomalies when context is considered. In this paper, we describe HyVarRec: A tool that relies on Satisfiability Modulo Theory (SMT) to detect and explain anomalies for context-aware SPLs.

Original languageEnglish
Title of host publicationSPLC 2017 - 21st International Systems and Software Product Line Conference, Proceedings
EditorsWalter Cazzola, Antonio Ruiz-Cortes, David Benavides, Marcello La Rosa, Roberto E. Lopez-Herrejon, Thomas Thum, Javier Troya, Maurice ter Beek, Oscar Diaz
Number of pages4
PublisherAssociation for Computing Machinery / Special Interest Group on Programming Languages
Publication date25. Sept 2017
Pages18-21
ISBN (Electronic)9781450351195
DOIs
Publication statusPublished - 25. Sept 2017
Externally publishedYes
Event21st International Systems and Software Product Line Conference, SPLC 2017 - Sevilla, Spain
Duration: 25. Sept 201729. Sept 2017

Conference

Conference21st International Systems and Software Product Line Conference, SPLC 2017
Country/TerritorySpain
CitySevilla
Period25/09/201729/09/2017
SponsorBigLever Software Inc, Hitachi

Keywords

  • Anomaly detection
  • Context-aware software engineering

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