M. Acher, B. Baudry, O. Barais, and J. Jézéquel, Customization and 3D Printing: A Challenging Playground for Software Product Lines, 18th International Software Product Line Conference, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01018937

M. Acher, P. Temple, J. Jézéquel, and J. A. Galindo, VaryLATEX: Learning Paper Variants That Meet Constraints, Proceedings of the 12th International Workshop on Variability Modelling of Software-Intensive Systems, pp.83-88, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01659161

C. Alcock, N. Hudson, and P. Chilana, Barriers to using, customizing, and Printing 3D designs on thingiverse, Proceedings of the 19th International Conference on Supporting Group Work, pp.195-199, 2016.

L. Ardissono, A. Felfernig, G. Friedrich, D. Jannach, R. Schäfer et al., A Framework for Rapid Development of Advanced Web-based Configurator Applications, ECAI'02, pp.618-622, 2002.

T. Asikainen, T. Mã?nnistã?, and T. Soininen, Using a configurator for modelling and configuring software product lines based on feature models, Workshop on Software Variability Management for Product Derivation, Software Product Line Conference, pp.24-35, 2004.

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

M. T. Beek, A. Legay, A. Lluch-lafuente, and A. Vandin, A framework for quantitative modeling and analysis of highly (re)configurable systems, IEEE Transactions on Software Engineering, 2018.

D. Benavides, S. Segura, P. Trinidad, and A. R. Cortés, FAMA: Tooling a Framework for the Automated Analysis of Feature Models, VaMoS'07, pp.129-134, 2007.

D. Beuche, Modeling and Building Software Product Lines with Pure: :Variants, 2008.

A. Classen, Q. Boucher, and P. Heymans, A text-based approach to feature modelling: Syntax and semantics of TVL, SCP, vol.76, pp.1130-1143, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00718291

M. Cordy and P. Heymans, Engineering Configurators for the Retail Industry: Experience Report and Challenges Ahead, ACM SAC '18, pp.2050-2057, 2018.

X. Ding, Product Configuration on the Semantic Web Using MultiAgent, ICNSC '08, pp.304-309, 2008.

A. Felfernig, L. Hotz, C. Bagley, and J. Tiihonen, Knowledge-based Configuration: From Research to Business Cases, 2014.

G. Fleischanderl, G. E. Friedrich, A. Haselböck, H. Schreiner, and M. Stumptner, Configuring Large Systems Using Generative Constraint Satisfaction, IEEE Intelligent Systems, vol.13, issue.4, pp.59-68, 1998.

E. Frank, M. A. Hall, and I. H. Witten, The WEKA Workbench. Online Appendix for "Data Mining: Practical Machine Learning Tools and Techniques, 2016.

P. Godefroid, M. Y. Levin, and D. Molnar, SAGE: Whitebox Fuzzing for Security Testing, Queue, Article, vol.20, 2012.

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

J. Guo, D. Yang, N. Siegmund, S. Apel, A. Sarkar et al., Dataefficient performance learning for configurable systems, Empirical Software Engineering, vol.23, pp.1826-1867, 2018.

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, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01829928

P. Jamshidi, M. Velez, C. Kästner, N. Siegmund, and P. Kawthekar, Transfer Learning for Improving Model Predictions in Highly Configurable Software, Proceedings of the 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), pp.31-41, 2017.

K. Kang, S. Cohen, J. Hess, W. Novak, and S. Peterson, Feature-Oriented Domain Analysis (FODA) Feasibility Study, 1990.

C. Kim, D. Marinov, S. Khurshid, D. Batory, S. Souto et al., SPLat: Lightweight Dynamic Analysis for Reducing Combinatorics in Testing Configurable Systems, ESEC/FSE, 2013.

, Optimal Reconfiguration of Dynamic Software Product Lines Based on Performance-Influence Models, Software Product Lines Conference (SPLC'18), 2018.

J. Martinez, J. Sottet, A. G. Frey, T. F. Bissyandé, T. Ziadi et al., Towards Estimating and Predicting User Perception on Software Product Variants, New Opportunities for Software Reuse-17th International Conference, pp.23-40, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01720519

F. Medeiros, C. Kästner, M. Ribeiro, R. Gheyi, and S. Apel, A Comparison of 10 Sampling Algorithms for Configurable Systems, ICSE'16, 2016.

V. Myllarniemi, M. Raatikainen, and T. Mannisto, Using a Configurator for Predictable Component Composition, 33rd EUROMICRO Conference on Software Engineering and Advanced Applications, pp.47-58, 2007.

V. Nair, Z. Yu, T. Menzies, and N. Siegmund, and Sven Apel. 2018. Finding Faster Configurations using FLASH

L. Oehlberg, W. Willett, and W. E. Mackay, Patterns of Physical Design Remixing in Online Maker Communities, Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, CHI 2015, Seoul, Republic of Korea, pp.639-648, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01263171

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.

D. Sabin and R. Weigel, Product configuration frameworks-a survey, IEEE Intelligent Systems and their Applications, vol.13, pp.42-49, 1998.

M. Santolucito, E. Zhai, R. Dhodapkar, A. Shim, and R. Piskac, Synthesizing configuration file specifications with association rule learning, Proceedings of the ACM on Programming Languages, vol.1, p.64, 2017.

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

N. Siegmund, A. Grebhahn, C. Kästner, and S. Apel, Performance-Influence Models for Highly Configurable Systems, ESEC/FSE'15, 2015.

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.

S. Souto, D. Gopinath, M. Amorim, and D. Marinov, Sarfraz Khurshid, and Don Batory. 2015. Faster Bug Detection for Software Product Lines with Incomplete Feature Models

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

P. Temple, J. Angel-galindo, M. Duarte, J. Acher, and . Jézéquel, Using Machine Learning to Infer Constraints for Product Lines, Software Product Line Conference (SPLC'16), 2016.
URL : https://hal.archives-ouvertes.fr/hal-01323446

. Thingiverse, MakerBot Industries, 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.

P. Valov, J. Petkovich, J. Guo, S. Fischmeister, and K. Czarnecki, Transferring Performance Prediction Models Across Different Hardware Platforms, Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering, pp.39-50, 2017.

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

L. Xu, F. Hutter, H. Holger, K. Hoos, and . Leyton-brown, SATzilla: Portfolio-based Algorithm Selection for SAT, J. Artif. Intell. Res, vol.32, pp.565-606, 2008.

Y. Zhang, J. Guo, E. Blais, and K. Czarnecki, Performance Prediction of Configurable Software Systems by Fourier Learning (T). In ASE'15, 2015.