Paper Info
Title
Anomaly Detection And Explanation In Context-Aware Software Product Lines
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.
Year
DOI
Venue
2017
10.1145/3109729.3109752
21ST INTERNATIONAL SYSTEM & SOFTWARE PRODUCT LINE CONFERENCE (SPLC 2017), VOL 2
Keywords
Field
DocType
Context-Aware Software Engineering, Anomaly Detection
Anomaly detection,Computer science,Modulo,Satisfiability,Boolean satisfiability problem,Software system,Theoretical computer science,Software,Feature model,Software product line
Conference
Citations 
PageRank 
References 
1
0.35
13
Authors
4
Name
Order
Citations
PageRank
Jacopo Mauro120926.74
Michael Nieke2394.12
Christoph Seidl320720.15
Ingrid Chieh Yu416418.53