Mind the gap! automated anomaly detection for potentially unbounded cardinality-based feature models (bibtex)
by Weckesser, Markus, Lochau, Malte, Schnabel, Thomas, Richerzhagen, Björn and Schürr, Andy
Abstract:
Feature models are frequently used for specifying variability of user-configurable software systems, e.g., software product lines. Numerous approaches have been developed for automating feature model validation concerning constraint consistency and absence of anomalies. As a crucial extension to feature models, cardinality annotations and respective constraints allow for multiple, and even potentially unbounded occurrences of feature instances within configurations. This is of particular relevance for user-adjustable application resources as prevalent, e.g., in cloud computing. However, a precise semantic characterization and tool support for automated and scalable validation of cardinality-based feature models is still an open issue. In this paper, we present a comprehensive formalization of cardinality-based feature models with potentially unbounded feature multiplicities. We apply a combination of ILP and SMT solvers to automate consistency checking and anomaly detection, including novel anomalies, e.g., interval gaps.We present evaluation results gained from our tool implementation showing applicability and scalability to larger-scale models. \textcopyright Springer-Verlag Berlin Heidelberg 2016.
Reference:
Mind the gap! automated anomaly detection for potentially unbounded cardinality-based feature models (Weckesser, Markus, Lochau, Malte, Schnabel, Thomas, Richerzhagen, Björn and Schürr, Andy), In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 9633, 2016.
Bibtex Entry:
@InProceedings{DBLP:conf/fase/WeckesserLSRS16,
  Title                    = {{Mind the gap! automated anomaly detection for potentially unbounded cardinality-based feature models}},
  Author                   = {Weckesser, Markus and Lochau, Malte and Schnabel, Thomas and Richerzhagen, Bj{\"{o}}rn and Sch\"urr, Andy},
  Booktitle                = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  Year                     = {2016},
  Pages                    = {158--175},
  Volume                   = {9633},

  Abstract                 = {Feature models are frequently used for specifying variability of user-configurable software systems, e.g., software product lines. Numerous approaches have been developed for automating feature model validation concerning constraint consistency and absence of anomalies. As a crucial extension to feature models, cardinality annotations and respective constraints allow for multiple, and even potentially unbounded occurrences of feature instances within configurations. This is of particular relevance for user-adjustable application resources as prevalent, e.g., in cloud computing. However, a precise semantic characterization and tool support for automated and scalable validation of cardinality-based feature models is still an open issue. In this paper, we present a comprehensive formalization of cardinality-based feature models with potentially unbounded feature multiplicities. We apply a combination of ILP and SMT solvers to automate consistency checking and anomaly detection, including novel anomalies, e.g., interval gaps.We present evaluation results gained from our tool implementation showing applicability and scalability to larger-scale models. {\textcopyright} Springer-Verlag Berlin Heidelberg 2016.},
  Doi                      = {10.1007/978-3-662-49665-7_10},
  ISBN                     = {9783662496640},
  ISSN                     = {16113349},
  Keywords                 = {Cardinalitybased feature models,Cloud-based systems,Integer Linear Programming (ILP),Software product lines,imotep},
  Mendeley-tags            = {imotep},
  Url                      = {http://dx.doi.org/10.1007/978-3-662-49665-7_10}
}
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