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 language | English |
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Title of host publication | SPLC 2017 - 21st International Systems and Software Product Line Conference, Proceedings |
Editors | Walter Cazzola, Antonio Ruiz-Cortes, David Benavides, Marcello La Rosa, Roberto E. Lopez-Herrejon, Thomas Thum, Javier Troya, Maurice ter Beek, Oscar Diaz |
Number of pages | 4 |
Publisher | Association for Computing Machinery / Special Interest Group on Programming Languages |
Publication date | 25. Sept 2017 |
Pages | 18-21 |
ISBN (Electronic) | 9781450351195 |
DOIs | |
Publication status | Published - 25. Sept 2017 |
Externally published | Yes |
Event | 21st International Systems and Software Product Line Conference, SPLC 2017 - Sevilla, Spain Duration: 25. Sept 2017 → 29. Sept 2017 |
Conference
Conference | 21st International Systems and Software Product Line Conference, SPLC 2017 |
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Country/Territory | Spain |
City | Sevilla |
Period | 25/09/2017 → 29/09/2017 |
Sponsor | BigLever Software Inc, Hitachi |
Keywords
- Anomaly detection
- Context-aware software engineering