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Anomaly Detection and Explanation in Context-Aware Software Product Lines by Jacopo Mauro, Michael Nieke, Christoph Seidl, and Ingrid Chieh Yu is a scholarly article available to read on EtoBox.

What is Anomaly Detection and Explanation in Context-Aware Software Product Lines about?

A software product line (SPL) uses a variability model, such as a feature model (FM), to describe the con guration options for a set of closely related software systems. Context-aware SPLs also consider possible environment conditions for their con guration 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 satis ability (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 Satis ability Modulo Theory (SMT) to detect and explain anomalies for context-aware SPLs.

Author

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

Publisher

ACM

Published

2017

Language

EN