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Mathematical Programming for Anomaly Analysis of Clafer Models

Published: 14 October 2018 Publication History

Abstract

Clafer combines UML-like class- and meta-modeling with feature-oriented variability-modeling and first-order logic constraints. The considerable expressiveness of Clafer mainly stems from its built-in variability constructs, multiplicity annotations and recursive model structures which yield a potentially unbounded number of valid model instances. As a result, automated reasoning about semantic properties like model consistency (i.e., existence of valid model instances) and anomalies (e.g., false cardinality bounds) is very challenging. Recent analysis techniques are inherently incomplete as they impose an a-priori finite search space with either manually or heuristically adjusted bounds. In this paper, we present a novel approach for automated search-space restriction for a considerably rich, yet decidable fragment of the Clafer language that guarantees sound and complete detection results for a wide range of semantic anomalies. Our approach employs principles from mathematical programming by encoding Clafer models as Mixed Integer Linear Programs (MILP). Our experimental evaluation shows remarkable improvements of runtime efficiency as well as effectiveness of anomaly detection as compared to existing techniques.

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  • (2024)Mapping Cardinality-based Feature Models to Weighted Automata over Featured Multiset SemiringsProceedings of the 28th ACM International Systems and Software Product Line Conference10.1145/3646548.3676539(1-11)Online publication date: 2-Sep-2024
  • (2024)Sampling Cardinality-Based Feature ModelsProceedings of the 18th International Working Conference on Variability Modelling of Software-Intensive Systems10.1145/3634713.3634719(46-55)Online publication date: 7-Feb-2024
  • (2022)Graph-Based Specification and Automated Construction of ILP ProblemsElectronic Proceedings in Theoretical Computer Science10.4204/EPTCS.374.3374(3-22)Online publication date: 21-Dec-2022
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cover image ACM Conferences
MODELS '18: Proceedings of the 21th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems
October 2018
478 pages
ISBN:9781450349499
DOI:10.1145/3239372
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 14 October 2018

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Author Tags

  1. Anomaly Detection
  2. Consistency Checks
  3. Software Product Lines

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MODELS '18 Paper Acceptance Rate 29 of 101 submissions, 29%;
Overall Acceptance Rate 144 of 506 submissions, 28%

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Cited By

View all
  • (2024)Mapping Cardinality-based Feature Models to Weighted Automata over Featured Multiset SemiringsProceedings of the 28th ACM International Systems and Software Product Line Conference10.1145/3646548.3676539(1-11)Online publication date: 2-Sep-2024
  • (2024)Sampling Cardinality-Based Feature ModelsProceedings of the 18th International Working Conference on Variability Modelling of Software-Intensive Systems10.1145/3634713.3634719(46-55)Online publication date: 7-Feb-2024
  • (2022)Graph-Based Specification and Automated Construction of ILP ProblemsElectronic Proceedings in Theoretical Computer Science10.4204/EPTCS.374.3374(3-22)Online publication date: 21-Dec-2022
  • (2020)REACT-ION: A Model-based Runtime Environment for Situation-aware AdaptationsACM Transactions on Autonomous and Adaptive Systems10.1145/348791915:4(1-29)Online publication date: 31-Dec-2020
  • (2020)REACT: A Model-Based Runtime Environment for Adapting Communication Systems2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS)10.1109/ACSOS49614.2020.00027(65-74)Online publication date: Aug-2020

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