Maurice H. ter Beek
ABSTRACT
The explicit management of variability in the development cycle of software-intensive systems has led to a plethora of modeling and analysis techniques tailored to deal with behavioral validation of such configurable systems. Most of the work, however, focuses on qualitative (i.e. functional) requirements. Recently, there is growing interest in variability modeling and analysis techniques that do explicitly consider quantitative (i.e. non-functional) requirements, such as dependability, energy consumption, security, and cost.
Today's software is embedded in a variety of smart and critical systems that run in environments where events occur randomly and affect the system, and to which it needs to adapt. Therefore, quantitative modeling and analysis is currently a hot topic. The panel on Quantitative Variability Modeling and Analysis (QSPL) discusses the latest quantitative techniques and how to apply them to variability modeling and analysis of software-intensive systems.
- M. Acher, P. Collet, P. Lahire, and R.B. France. 2013. FAMILIAR: A domain-specific language for large scale management of feature models. Sci. Comput. Program. 78, 6 (2013), 657--681. Google Scholar
Digital Library
- S.S. Bauer, U. Fahrenberg, L. Juhl, K.G. Larsen, A. Legay, and C.R. Thrane. 2013. Weighted modal transition systems. Form. Method. Sys. Design 42 (2013), 193--220. Google ScholarDigital Library
- M.H. ter Beek and E.P. de Vink. 2014. Towards Modular Verification of Software Product Lines with mCRL2. In Proc. ISoLA 2014 (LNCS), Vol. 8802. 368--385. Google ScholarDigital Library
- M.H. ter Beek and E.P. de Vink. 2014. Using mCRL2 for the Analysis of Software Product Lines. In Proc. FormaliSE@ICSE 2014. ACM, 31--37. Google ScholarDigital Library
- M.H. ter Beek, E.P. de Vink, and T.A.C. Willemse. 2017. Family-Based Model Checking with mCRL2. In Proc. FASE 2017 (LNCS), Vol. 10202. 387--405. Google ScholarDigital Library
- M.H. ter Beek, A. Fantechi, S. Gnesi, and F. Mazzanti. 2016. Modeling and analysing variability in product families: Model checking of modal transition systems with variability constraints. J. Log. Algebr. Meth. Program. 85, 2 (2016), 287--315.Google ScholarCross Ref
- M.H. ter Beek and A. Legay (Eds.). 2019. Special Section: Quantitative Variability Modelling and Analysis. LNCS Trans. Found. Mastering Change 2 (2019).Google Scholar
- M.H. ter Beek, A. Legay, A. Lluch Lafuente, and A. Vandin. 2015. Statistical Analysis of Probabilistic Models of Software Product Lines with Quantitative Constraints. In Proc. SPLC 2015. ACM, 11--15. Google ScholarDigital Library
- M.H. ter Beek, A. Legay, A. Lluch Lafuente, and A. Vandin. 2016. Statistical Model Checking for Product Lines. In Proc. ISoLA 2016 (LNCS), Vol. 9952. 114--133.Google Scholar
- M.H. ter Beek, A. Legay, A. Lluch Lafuente, and A. Vandin. 2018. A framework for quantitative modeling and analysis of highly (re)configurable systems. IEEE Trans. Softw. Eng. (2018).Google Scholar
- M.H. ter Beek, A. Legay, A. Lluch Lafuente, and A. Vandin. 2018. QFLan: A Tool for the Quantitative Analysis of Highly Reconfigurable Systems. In Proc. FM 2018 (LNCS), Vol. 10951. 329--337.Google Scholar
- M.H. ter Beek, F. Mazzanti, and A. Sulova. 2012. VMC: A Tool for Product Variability Analysis. In Proc. FM 2012 (LNCS), Vol. 7436. 450--454.Google Scholar
- T. Belder, M.H. ter Beek, and E.P. de Vink. 2015. Coherent branching feature bisimulation. Electron. Proc. Theor. Comput. Sci. 220, 3 (2015), 14--30.Google ScholarCross Ref
- P. Bouyer, U. Fahrenberg, K.G. Larsen, N. Markey, J. Ouaknine, and J. Worrell. 2018. Model checking real-time systems. In Handbook of Model Checking, E.M. Clarke, T.A. Henzinger, H. Veith, and R. Bloem (Eds.). Chapter 29, 1001--1046.Google Scholar
- R. Calinescu, M. Češka, S. Gerasimou, M. Kwiatkowska, and N. Paoletti. 2018. Efficient synthesis of robust models for stochastic systems. J. Sys. Softw. 143 (2018), 140--158.Google ScholarCross Ref
- P. Chrszon, C. Dubslaff, S. Klüppelholz, and C. Baier. 2016. Family-Based Modeling and Analysis for Probabilistic Systems: Featuring ProFeat. In Proc. FASE 2016 (LNCS), Vol.9633.287--304.Google Scholar
- P. Chrszon, C. Dubslaff, S. Klüppelholz, and C. Baier. 2018. ProFeat: feature-oriented engineering for family-based probabilistic model checking. Form. Asp. Comp. 30, 1 (2018), 45--75. Google ScholarDigital Library
- A. Classen, Q. Boucher, and P. Heymans. 2011. A text-based approach to feature modelling: Syntax and semantics of TVL. Sci. Comput. Program. 76, 12 (2011), 1130--1143. Google ScholarDigital Library
- A. Classen, M. Cordy, P. Heymans, A. Legay, and P.-Y. Schobbens. 2012. Model checking software product lines with SNIP. Int. J. Softw. Tools Technol. Transf. 14, 5 (2012), 589--612. Google ScholarDigital Library
- A. Classen, M. Cordy, P. Heymans, A. Legay, and P.-Y. Schobbens. 2014. Formal semantics, modular specification, and symbolic verification of product-line behaviour. Sci. Comput. Program. 80, B (2014), 416--439.Google Scholar
- A. Classen, M. Cordy, P.-Y. Schobbens, P. Heymans, A. Legay, and J.-F. Raskin. 2013. Featured Transition Systems: Foundations for Verifying Variability-Intensive Systems and Their Application to LTL Model Checking. IEEE Trans. Softw. Eng. 39, 8 (2013), 1069--1089. Google ScholarDigital Library
- A. Classen, P. Heymans, P.-Y. Schobbens, and A. Legay. 2011. Symbolic model checking of software product lines. In Proc. ICSE 2011. ACM, 321--330. Google ScholarDigital Library
- A. Classen, P. Heymans, P.-Y. Schobbens, A. Legay, and J.-F. Raskin. 2010. Model Checking Lots of Systems: Efficient Verification of Temporal Properties in Software Product Lines. In Proc. ICSE 2010. ACM, 335--344. Google ScholarDigital Library
- M. Cordy, A. Classen, P. Heymans, P.-Y. Schobbens, and A. Legay. 2013. ProVe-Lines: a product line of verifiers for software product lines. In Proc. SPLC 2013, Vol. 2. ACM, 141--146. Google ScholarDigital Library
- M. Cordy, A. Classen, G. Perrouin, P.-Y. Schobbens, P. Heymans, and A. Legay. 2012. Simulation-Based Abstractions for Software Product-Line Model Checking. In Proc. ICSE 2012. IEEE, 672--682. Google ScholarDigital Library
- M. Cordy and A. Legay. 2019. Verification and Abstraction of Real-Time Variability-Intensive Systems. Trans. Found. Mastering Change 2 (2019).Google Scholar
- M. Cordy, P.-Y. Schobbens, P. Heymans, and A. Legay. 2012. Behavioural Modelling and Verification of Real-Time Software Product Lines. In Proc. SPLC 2012. ACM, 66--75. Google ScholarDigital Library
- M. Cordy, P.-Y. Schobbens, P. Heymans, and A. Legay. 2013. Beyond Boolean Product-Line Model Checking: Dealing with Feature Attributes and Multifeatures. In Proc. ICSE 2013. IEEE, 472--481. Google ScholarDigital Library
- A. Dimovski, A. Al-Sibahi, C. Brabrand, and A. Wąsowski. 2015. Family-Based Model Checking using Off-the-Shelf Model Checkers. In Proc. SPLC 2015. ACM, 397. Google ScholarDigital Library
- C. Dubslaff, C. Baier, and S. Klüppelholz. 2015. Probabilistic Model Checking for Feature-Oriented Systems. In Trans. AOSD XII. LNCS, Vol. 8989. 180--220.Google Scholar
- C. Dubslaff, S. Klüppelholz, and C. Baier. 2014. Probabilistic Model Checking for Energy Analysis in Software Product Lines. In Proc. MODULARITY 2014. ACM, 169--180. Google ScholarDigital Library
- M. Erwig and E. Walkingshaw. 2011. The Choice Calculus: A Representation for Software Variation. ACM Trans. Softw. Eng. Methodol. 21, 1 (2011). Google ScholarDigital Library
- M. Faella, A. Legay, and M. Stoelinga. 2008. Model checking quantitative linear time logic. Electron. Notes Theor. Comput. Sci. 220, 3 (2008), 61--77. Google ScholarDigital Library
- U. Fahrenberg and A. Legay. 2014. The quantitative linear-time--branching-time spectrum. Theoret. Comput. Sci. 538 (2014), 54--69.Google ScholarCross Ref
- U. Fahrenberg and A. Legay. 2017. Featured Weighted Automata. In Proc. FormaliSE@ICSE 2017. IEEE, 51--57. Google ScholarDigital Library
- U. Fahrenberg and A. Legay. 2019. Quantitative Properties of Featured Automata. Trans. Found. Mastering Change 2 (2019).Google Scholar
- A. Filieri, G. Tamburrelli, and C. Ghezzi. 2016. Supporting Self-Adaptation via Quantitative Verification and Sensitivity Analysis at Run Time. IEEE Trans. Softw. Eng. 42, 1 (2016), 75--99.Google ScholarDigital Library
- M. Gerhold and M. Stoelinga. 2018. Model-based testing of probabilistic systems. Form. Asp. Comp. 30, 1 (2018), 77--106. Google ScholarDigital Library
- C. Ghezzi and A. Molzam Sharifloo. 2013. Model-based verification of quantitative non-functional properties for software product lines. Inform. Softw. Technol. 55, 3 (2013), 508--524. Google ScholarDigital Library
- L. Herrmann, M. Küttler, T. Stumpf, C. Baier, H. Härtig, and S. Klüppelholz. 2019. Configuration of Inter-Process Communication with Probabilistic Model Checking. Trans. Found. Mastering Change 2 (2019).Google Scholar
- T. Hune, J. Romijn, M. Stoelinga, and F. Vaandrager. 2002. Linear parametric model checking of timed automata. J. Log. Algebr. Program. 52--53 (2002), 183--220.Google Scholar
- P. Juodisius, A. Sarkar, R.R. Mukkamala, M. Antkiewicz, K. Czarnecki, and A. Wąsowski. 2019. Clafer: Lightweight Modeling of Structure, Behaviour, and Variability. Programming 3, 1 (2019), 657--681.Google Scholar
- M. Kowal, I. Schaefer, and M. Tribastone. 2014. Family-Based Performance Analysis of Variant-Rich Software Systems. In Proc. FASE 2014 (LNCS), Vol. 8411. 94--108. Google ScholarDigital Library
- A. Legay and G. Perrouin. 2017. On Quantitative Requirements for Product Lines. In Proc. VaMoS 2017. ACM, 2--4. Google ScholarDigital Library
- M. Lochau, S. Mennicke, H. Baller, and L. Ribbeck. 2014. DeltaCCS: A Core Calculus for Behavioral Change. In Proc. ISoLA 2014 (LNCS), Vol. 8802. 320--335. Google ScholarDigital Library
- R. Muschevici, J. Proença, and D. Clarke. 2016. Feature Nets: behavioural modelling of software product lines. Softw. Sys. Model. 15, 4 (2016), 1181--1206. Google ScholarDigital Library
- R. Olaechea, U. Fahrenberg, J.M. Atlee, and A. Legay. 2016. Long-term Average Cost in Featured Transition Systems. In Proc. SPLC 2016. ACM, 109--118. Google ScholarDigital Library
- A. Remke and M. Stoelinga. 2014. Stochastic Model Checking. LNCS, Vol. 8453.Google Scholar
- G.N. Rodrigues, V. Alves, V. Nunes, A. Lanna, M. Cordy, P.-Y. Schobbens, A. Molzam Sharifloo, and A. Legay. 2015. Modeling and Verification for Probabilistic Properties in Software Product Lines. In Proc. HASE 2015. IEEE, 173--180. Google ScholarDigital Library
- M. Stoelinga. 2002. An introduction to probabilistic automata. Bulletin EATCS 78 (2002), 176--198.Google Scholar
- T. Thüm, S. Apel, C. Kästner, I. Schaefer, and G. Saake. 2014. Classification and survey of analysis strategies for software product lines. ACM Comp. Surv. 47, 1 (2014). Google ScholarDigital Library
Index Terms
- Quantitative Variability Modeling and Analysis
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