An Approach to Derive Usage Models Variants for Model-Based Testing

Testing techniques in industry are not yet adapted for product line engineering (PLE). In particular, Model-based Testing (MBT), a technique that allows to automatically generate test cases from requirements, lacks support for managing variability (differences) among a set of related product. In this paper, we present an approach to equip usage models, a widely used formalism in MBT, with variability capabilities. Formal correspondences are established between a variability model, a set of functional requirements, and a usage model. An algorithm then exploits the traceability links to automatically derive a usage model variant from a desired set of selected features. The approach is integrated into the professional MBT tool MaTeLo and is currently used in industry.

[1]  Andy Schürr,et al.  A Survey of Model-Based Software Product Lines Testing , 2011, Model-Based Testing for Embedded Systems.

[2]  Mathieu Acher,et al.  Deriving Usage Model Variants for Model-Based Testing: An Industrial Case Study , 2014, 2014 19th International Conference on Engineering of Complex Computer Systems.

[3]  Gunter Saake,et al.  A Classification and Survey of Analysis Strategies for Software Product Lines , 2014, ACM Comput. Surv..

[4]  Klaus Pohl,et al.  Software Product Line Engineering , 2005 .

[5]  Pierre-Yves Schobbens,et al.  Disambiguating the Documentation of Variability in Software Product Lines: A Separation of Concerns, Formalization and Automated Analysis , 2007, 15th IEEE International Requirements Engineering Conference (RE 2007).

[6]  Raymond A. Marie,et al.  Reliability estimation for statistical usage testing using Markov chains , 2004, 15th International Symposium on Software Reliability Engineering.

[7]  Hamza Samih,et al.  MPLM -- MaTeLo Product Line Manager , 2014 .

[8]  Ina Schieferdecker,et al.  Model-Based Testing of Embedded Systems Exemplified for the Automotive Domain , 2010 .

[9]  Malte Lochau,et al.  Incremental Model-Based Testing of Delta-Oriented Software Product Lines , 2012, TAP@TOOLS.

[10]  Bruno Legeard,et al.  A taxonomy of model‐based testing approaches , 2012, Softw. Test. Verification Reliab..

[11]  Thomas Thelin,et al.  Practical experiences with statistical usage testing , 2003, Eleventh Annual International Workshop on Software Technology and Engineering Practice.

[12]  Pierre-Yves Schobbens,et al.  Towards statistical prioritization for software product lines testing , 2013, VaMoS.

[13]  Jacques Klein,et al.  Automated and Scalable T-wise Test Case Generation Strategies for Software Product Lines , 2010, 2010 Third International Conference on Software Testing, Verification and Validation.

[14]  Klaus Pohl,et al.  Software Product Line Engineering - Foundations, Principles, and Techniques , 2005 .

[15]  Pierre-Yves Schobbens,et al.  Model checking lots of systems: efficient verification of temporal properties in software product lines , 2010, 2010 ACM/IEEE 32nd International Conference on Software Engineering.

[16]  L. Breuer Introduction to Stochastic Processes , 2022, Statistical Methods for Climate Scientists.

[17]  Hamza Samih,et al.  MPLM - MaTeLo product line manager: [relating variability modelling and model-based testing] , 2014, SPLC '14.

[18]  Pierre-Yves Schobbens,et al.  A Vision for Behavioural Model-Driven Validation of Software Product Lines , 2012, ISoLA.

[19]  Ana María Sánchez Melero,et al.  Facultad de Informática , 2007 .

[20]  Stephan Weißleder,et al.  Top-Down and Bottom-Up Approach for Model-Based Testing of Product Lines , 2013, MBT.

[21]  Øystein Haugen,et al.  An algorithm for generating t-wise covering arrays from large feature models , 2012, SPLC '12.

[22]  Arnaud Gotlieb,et al.  Minimum Pairwise Coverage Using Constraint Programming Techniques , 2012, 2012 IEEE Fifth International Conference on Software Testing, Verification and Validation.

[23]  Justyna Zander-Nowicka,et al.  Model-based Testing of Real-Time Embedded Systems in the Automotive Domain , 2009 .