M. Barreno, B. Nelson, R. Sears, D. Anthony, J. Joseph et al., Can machine learning be secure, Proceedings of the 2006 ACM Symposium on Information, computer and communications security, pp.16-25, 2006.

D. S. Batory, Feature Models, Grammars, and Propositional Formulas, SPLC'05, vol.3714, pp.7-20, 2005.

R. Bellman, Dynamic Programming, 1957.

D. Benavides, S. Segura, and A. Ruiz-cortes, Automated Analysis of Feature Models 20 years Later: a Literature Review, Information Systems, vol.35, pp.615-636, 2010.

T. Berger, R. Rublack, D. Nair, J. M. Atlee, M. Becker et al., A Survey of Variability Modeling in Industrial Practice, Proceedings of the Seventh International Workshop on Variability Modelling of Software-intensive Systems (VaMoS '13), 2013.

B. Biggio, I. Corona, D. Maiorca, B. Nelson, N. ?rndi? et al., Evasion attacks against machine learning at test time, Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp.387-402, 2013.

B. Biggio, L. Didaci, G. Fumera, and F. Roli, Poisoning attacks to compromise face templates, 2013 International Conference on Biometrics (ICB), pp.1-7, 2013.

B. Biggio, G. Fumera, and F. Roli, Pattern recognition systems under attack: Design issues and research challenges, International Journal of Pattern Recognition and Artificial Intelligence, vol.28, p.1460002, 2014.

B. Biggio, G. Fumera, and F. Roli, Security evaluation of pattern classifiers under attack, IEEE transactions on knowledge and data engineering, vol.26, pp.984-996, 2014.

B. Biggio, B. Nelson, and P. Laskov, Poisoning Attacks Against Support Vector Machines, Proceedings of the 29th International Coference on International Conference on Machine Learning (ICML'12), pp.1467-1474, 2012.

B. Biggio and F. Roli, Wild patterns: Ten years after the rise of adversarial machine learning, Pattern Recognition, vol.84, pp.317-331, 2018.

E. Bodden, T. Tolêdo, M. Ribeiro, C. Brabrand, P. Borba et al., SPL LIFT : statically analyzing software product lines in minutes instead of years, ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI '13, pp.355-364, 2013.

Q. Boucher, A. Classen, P. Faber, and P. Heymans, Introducing TVL, a Text-based Feature Modelling, Fourth International Workshop on Variability Modelling of Software-Intensive Systems, vol.37, pp.159-162, 2010.

T. Brown, D. Mane, A. Roy, M. Abadi, and J. Gilmer, Adversarial Patch, 2017.

M. Buhrmester, T. Kwang, and S. D. Gosling, Amazon's Mechanical Turk: A new source of inexpensive, yet high-quality, data? Perspectives on psychological science, vol.6, pp.3-5, 2011.

V. Nitesh, K. W. Chawla, L. O. Bowyer, W. Hall, and . Kegelmeyer, SMOTE: synthetic minority over-sampling technique, Journal of artificial intelligence research, vol.16, pp.321-357, 2002.

A. Classen, Q. Boucher, and P. Heymans, A Text-based Approach to Feature Modelling: Syntax and Semantics of TVL, Science of Computer Programming, Special Issue on Software Evolution, Adaptability and Variability, vol.76, pp.1130-1143, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00718291

P. Clements and L. M. Northrop, Software Product Lines : Practices and Patterns, 2001.

J. Davril, P. Heymans, G. Bécan, and M. Acher, On Breaking The Curse of Dimensionality in Reverse Engineering Feature Models, 17th International Configuration Workshop (17th International Configuration Workshop), 2015.
URL : https://hal.archives-ouvertes.fr/hal-01243571

A. Demontis, M. Melis, M. Pintor, M. Jagielski, B. Biggio et al., On the Intriguing Connections of Regularization, Input Gradients and Transferability of Evasion and Poisoning Attacks, 2018.

A. Demontis, M. Melis, M. Pintor, M. Jagielski, B. Biggio et al., Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks, 28th USENIX Security Symposium (USENIX Security 19). USENIX Association, 2019.

K. Guneet-s-dhillon, . Azizzadenesheli, C. Zachary, J. Lipton, J. Bernstein et al., Stochastic activation pruning for robust adversarial defense, 2018.

W. Richard, X. Dosselman, and Y. Dong, No-Reference Noise and Blur Detection via the Fourier Transform, 2012.

S. Gamaleldin-f-elsayed, B. Shankar, N. Cheung, A. Papernot, I. Kurakin et al., Adversarial Examples that Fool both Human and Computer Vision, 2018.

I. Evtimov, K. Eykholt, E. Fernandes, T. Kohno, B. Li et al., Robust Physical-World Attacks on Deep Learning Models, 2017.

J. Duarte, M. Alférez, M. Acher, B. Baudry, and D. Benavides, A Variability-Based Testing Approach for Synthesizing Video Sequences, ISSTA '14: International Symposium on Software Testing and Analysis, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01003148

I. Goodfellow, J. Pouget-abadie, M. Mirza, B. Xu, D. Warde-farley et al., Generative adversarial nets, Advances in neural information processing systems, pp.2672-2680, 2014.

C. Guo and M. Rana, Moustapha Cisse, and Laurens van der Maaten. 2017. Countering adversarial images using input transformations, 2017.

J. Guo, K. Czarnecki, S. Apel, N. Siegmund, and A. Wasowski, Variability-aware performance prediction: A statistical learning approach, ASE, 2013.

A. Halin, A. Nuttinck, M. Acher, X. Devroey, G. Perrouin et al., Test them all, is it worth it? Assessing configuration sampling on the JHipster Web development stack, Empirical Software Engineering, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01829928

A. Halin, A. Nuttinck, M. Acher, X. Devroey, G. Perrouin et al., Test them all, is it worth it? Assessing configuration sampling on the JHipster Web development stack, Empirical Software Engineering, vol.24, pp.674-717, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01829928

R. Ierusalimschy, Programming in Lua, Second Edition, Lua.Org, 2006.

C. Kaner, J. Bach, and B. Pettichord, Lessons Learned in Software Testing, 2001.

A. Knüppel, T. Thüm, S. Mennicke, J. Meinicke, and I. Schaefer, Is There a Mismatch between Real-World Feature Models and Product-Line Research, Software Engineering und Software Management, pp.53-54, 2018.

A. Kurakin, I. Goodfellow, and S. Bengio, Adversarial examples in the physical world, 2016.

A. Legay and G. Perrouin, On Quantitative Requirements for Product Lines, Proceedings of the Eleventh International Workshop on Variability Modelling of Software-intensive Systems (VAMOS '17, 2017.

A. Madry, A. Makelov, and L. Schmidt, Dimitris Tsipras, and Adrian Vladu. 2017. Towards deep learning models resistant to adversarial attacks, 2017.

F. Medeiros, C. Kästner, M. Ribeiro, R. Gheyi, and S. Apel, A Comparison of 10 Sampling Algorithms for Configurable Systems, Proceedings of the 38th International Conference on Software Engineering (ICSE '16), 2016.

, , pp.643-654

S. Nadi, T. Berger, C. Kästner, and K. Czarnecki, Mining configuration constraints: static analyses and empirical results, 36th International Conference on Software Engineering, ICSE '14, pp.140-151, 2014.

V. Nair, T. Menzies, N. Siegmund, and S. Apel, Using bad learners to find good configurations, Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering, pp.257-267, 2017.

B. Nelson, M. Barreno, F. J. Chi, A. D. Joseph, U. Benjamin-ip-rubinstein et al., Exploiting Machine Learning to Subvert Your Spam Filter, LEET, vol.8, pp.1-9, 2008.

J. Oh, D. S. Batory, M. Myers, and N. Siegmund, Finding near-optimal configurations in product lines by random sampling, Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering, pp.61-71, 2017.

N. Papernot, P. Mcdaniel, S. Jha, M. Fredrikson, Z. B. Celik et al., The Limitations of Deep Learning in Adversarial Settings, 2016 IEEE European Symposium on Security and Privacy, pp.372-387, 2016.

K. Pei, Y. Cao, J. Yang, and S. Jana, DeepXplore: Automated Whitebox Testing of Deep Learning Systems, Proceedings of the 26th Symposium on Operating Systems Principles (SOSP '17, pp.1-18, 2017.

J. A. Pereira, H. Martin, M. Acher, J. Jã?zã?quel, G. Botterweck et al., Learning Software Configuration Spaces: A Systematic Literature Review, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02148791

Q. Plazar, M. Acher, G. Perrouin, X. Devroey, and M. Cordy, Uniform Sampling of SAT Solutions for Configurable Systems: Are We There Yet, 12th IEEE Conference on Software Testing, Validation and Verification, ICST 2019, pp.240-251, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01991857

K. Pohl, G. Böckle, and F. J. Van-der-linden, Software Product Line Engineering: Foundations, Principles and Techniques, 2005.

A. Sarkar, J. Guo, N. Siegmund, S. Apel, and K. Czarnecki, CostEfficient Sampling for Performance Prediction of Configurable Systems (T), ASE'15, 2015.

P. Schobbens, P. Heymans, J. Trigaux, and Y. Bontemps, Generic semantics of feature diagrams, Comput. Netw, vol.51, pp.456-479, 2007.

M. Sharif, S. Bhagavatula, L. Bauer, and M. Reiter, Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition, Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp.1528-1540, 2016.

N. Siegmund, A. Grebhahn, C. Kästner, and S. Apel,

, Performance-Influence Models for Highly Configurable Systems, ESEC/FSE'15

N. Siegmund, M. Rosenmüller, C. Kästner, P. G. Giarrusso, S. Apel et al., Scalable Prediction of Non-functional Properties in Software Product Lines: Footprint and Memory Consumption, Inf. Softw. Technol, 2013.

N. Siegmund, S. Sobernig, and S. Apel, Attributed Variability Models: Outside the Comfort Zone, Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering, pp.268-278, 2017.

D. Strüber, J. Rubin, T. Arendt, M. Chechik, G. Taentzer et al., Variability-based model transformation: formal foundation and application, Formal Asp. Comput, vol.30, pp.133-162, 2018.

P. Temple, M. Acher, J. Jézéquel, and O. Barais, Learning Contextual-Variability Models, IEEE Software, vol.34, pp.64-70, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01659137

P. Temple, J. Duarte, M. Acher, and J. Jézéquel, Using Machine Learning to Infer Constraints for Product Lines, Software Product Line Conference (SPLC), 2016.
URL : https://hal.archives-ouvertes.fr/hal-01323446

M. H. Ter-beek, A. Fantechi, S. Gnesi, and F. Mazzanti, Modelling and analysing variability in product families: Model checking of modal transition systems with variability constraints, J. Log. Algebr. Meth. Program, vol.85, pp.287-315, 2016.

M. H. Ter-beek, A. Fantechi, S. Gnesi, and L. Semini, Variability-Based Design of Services for Smart Transportation Systems, Leveraging Applications of Formal Methods, Verification and Validation: Discussion, Dissemination, Applications -7th International Symposium, pp.465-481, 2016.

M. H. Ter-beek and A. Legay, Quantitative Variability Modeling and Analysis, Proceedings of the 13th International Workshop on Variability Modelling of Software-Intensive Systems (VAMOS '19, vol.13, 2019.

T. Thüm, S. Apel, C. Kästner, I. Schaefer, and G. Saake, A Classification and Survey of Analysis Strategies for Software Product Lines, Comput. Surveys, 2014.

M. Varshosaz, M. Al-hajjaji, T. Thüm, and T. Runge, A classification of product sampling for software product lines, Proceeedings of the 22nd International Systems and Software Product Line Conference, vol.1, pp.1-13, 2018.

M. Zhang, Y. Zhang, L. Zhang, C. Liu, and S. Khurshid, DeepRoad: GAN-based Metamorphic Testing and Input Validation Framework for Autonomous Driving Systems, Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering, pp.132-142, 2018.