M. Acher, H. Martin, J. A. Pereira, A. Blouin, J. Jézéquel et al., Learning Very Large Configuration Spaces: What Matters for Linux Kernel Sizes, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02314830

M. Acher, P. Temple, J. Jezequel, A. José, J. Galindo et al., 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

M. Benoit-amand, P. Cordy, M. Heymans, P. Acher, J. Temple et al., Towards Learning-Aided Configuration in 3D Printing: Feasibility Study and Application to Defect Prediction, Proceedings of the 13th International Workshop on Variability Modelling of Software-Intensive Systems, p.7, 2019.

A. Arcuri and L. Briand, A Practical Guide for Using Statistical Tests to Assess Randomized Algorithms in Software Engineering, Proceedings of the 33rd International Conference on Software Engineering (ICSE '11), pp.1-10, 2011.

A. Arcuri and L. Briand, Formal Analysis of the Probability of Interaction Fault Detection Using Random Testing, IEEE Transactions on Software Engineering, vol.38, pp.1088-1099, 2012.

L. Bao, X. Liu, Z. Xu, and B. Fang, AutoConfig: Automatic Configuration Tuning for Distributed Message Systems, IEEE/ACM International Conference on Automated Software Engineering (ASE), pp.29-40, 2018.

L. Breiman, Random forests, Machine learning, vol.45, pp.5-32, 2001.

S. Chakraborty, D. J. Fremont, K. S. Meel, A. Sanjit, M. Y. Seshia et al., On Parallel Scalable Uniform SAT Witness Generation, Tools and Algorithms for the Construction and Analysis of Systems TACAS'15, pp.304-319, 2015.

S. Chakraborty, S. Kuldeep, M. Y. Meel, and . Vardi, A Scalable and Nearly Uniform Generator of SAT Witnesses, International Conference on Computer Aided Verification, pp.608-623, 2013.

S. Chen, Y. Liu, I. Gorton, and A. Liu, Performance Prediction of Component-Based Applications, Journal of Systems and Software, vol.74, pp.35-43, 2005.

. Myra-b-cohen, B. Matthew, . Dwyer, and J. Shi, Constructing Interaction Test Suites for Highly-Configurable Systems in the Presence of Constraints: A Greedy Approach, IEEE TSE, vol.34, pp.633-650, 2008.

L. De-moura and N. Bjørner, Z3: An efficient SMT solver, International conference on Tools and Algorithms for the Construction and Analysis of Systems, pp.337-340, 2008.

A. Fisher, C. Rudin, and F. Dominici, All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously, 2018.

A. Grebhahn, C. Rodrigo, N. Siegmund, J. Francisco, S. Gaspar et al., Performance-Influence Models of Multigrid Methods: A Case Study on Triangular Grids, Concurrency and Computation: Practice and Experience, vol.29, p.4057, 2017.

J. Guo, K. Czarnecki, S. Apel, N. Siegmund, and A. Wasowski, Variability-Aware Performance Prediction: A Statistical Learning Approach, Automated Software Engineering (ASE), pp.301-311, 2013.

J. Guo, J. H. Liang, K. Shi, D. Yang, J. Zhang et al., SMTIBEA: A Hybrid Multi-Objective Optimization Algorithm for Configuring Large Constrained Software Product Lines, Software & Systems Modeling, 2017.

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

C. Henard, M. Papadakis, G. Perrouin, J. Klein, P. Heymans et al., Bypassing the Combinatorial Explosion: Using Similarity to Generate and Prioritize T-Wise Test Configurations for Software Product Lines, IEEE Trans. Software Eng, 2014.

R. Heradio, D. Fernández-amorós, C. Mayr-dorn, and A. Egyed, Supporting the Statistical Analysis of Variability Models, 41st International Conference on Software Engineering, ICSE, pp.843-853, 2019.

P. Jamshidi, J. Cámara, B. Schmerl, C. Kästner, and D. Garlan, Machine Learning Meets Quantitative Planning: Enabling Self-Adaptation in Autonomous Robots, 2019.

P. Jamshidi, N. Siegmund, M. Velez, A. Patel, and Y. Agarwal, Transfer Learning for Performance Modeling of Configurable Systems: An Exploratory Analysis, IEEE/ACM International Conference on Automated Software Engineering (ASE), pp.497-508, 2017.

P. Jamshidi, M. Velez, C. Kästner, and N. Siegmund, Learning to Sample: Exploiting Similarities Across Environments to Learn Performance Models for Configurable Systems, Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pp.71-82, 2018.

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

Ø. Martin-fagereng-johansen, F. Haugen, and . Fleurey, An Algorithm for Generating t-Wise Covering Arrays from Large Feature Models, Proceedings of the 16th International Software Product Line Conference on -SPLC '12 -volume, vol.1, p.46, 2012.

C. Kaltenecker, A. Grebhahn, N. Siegmund, J. Guo, and S. Apel, Distance-Based Sampling of Software Configuration Spaces, Proceedings of the International Conference on Software Engineering (ICSE), 2019.

S. Kolesnikov, N. Siegmund, C. Kästner, and S. Apel, On the Relation of External and Internal Feature Interactions: A Case Study, 2017.

S. Kolesnikov, N. Siegmund, C. Kästner, A. Grebhahn, and S. Apel, Tradeoffs in Modeling Performance of Highly Configurable Software Systems. Software & Systems Modeling, vol.18, pp.2265-2283, 2019.

T. Krismayer, R. Rabiser, and P. Grünbacher, Mining Constraints for Event-Based Monitoring in Systems of Systems, IEEE/ACM International Conference on Automated Software Engineering (ASE), pp.826-831, 2017.

H. William, W. Kruskal, and . Wallis, Use of Ranks in One-Criterion Variance Analysis, Journal of the American statistical Association, vol.47, pp.583-621, 1952.

D. R. Kuhn, D. R. Wallace, and A. M. Gallo, Software fault interactions and implications for software testing, IEEE Transactions on Software Engineering, vol.30, issue.6, pp.418-421, 2004.

L. Daniel, A. Berre, and . Parrain, The SAT4J library, Release 2.2, System Description, Journal on Satisfiability, Boolean Modeling and Computation, vol.7, pp.59-64, 2010.

H. Levene, Robust Tests for Equality of Variances. Contributions to probability and statistics, Essays in honor of Harold Hotelling, pp.279-292, 1961.

M. Lillack, J. Müller, and U. W. Eisenecker, Improved Prediction of Non-Functional Properties in Software Product Lines with Domain Context. Software Engineering, 2013.

B. Henry, D. Mann, and . Whitney, On a Test of Whether One of Two Random Variables is Stochastically Larger than the Other. The annals of mathematical statistics, pp.50-60, 1947.

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, pp.643-654, 2016.

M. Mendonca, A. Wasowski, K. Czarnecki, and D. Cowan, Efficient Compilation Techniques for Large Scale Feature Models, Int'l Conference on Generative programming and component engineering, pp.13-22, 2008.

C. Molnar, Interpretable Machine Learning, 2019.

D. Munoz, J. Oh, M. Pinto, L. Fuentes, and D. S. Batory, Uniform Random Sampling Product Configurations of Feature Models that Have Numerical Features, International Systems and Software Product Line Conference (SPLC), vol.39, pp.1-39, 2019.

B. I-made-murwantara, L. L. Bordbar, and . Minku, Measuring Energy Consumption for Web Service Product Configuration, Proceedings of the 16th International Conference on Information Integration and Web-based Applications & Services (iiWAS), pp.224-228, 2014.

V. Nair, T. Menzies, N. Siegmund, and S. Apel, Using Bad Learners to Find Good Configurations, Proceedings of the European Software Engineering Conference/Foundations of Software Engineering (ESEC/FSE, pp.257-267, 2017.

V. Nair, T. Menzies, N. Siegmund, and S. Apel, Faster Discovery of Faster System Configurations with Spectral Learning, Automated Software Engineering, pp.1-31, 2018.

V. Nair, Z. Yu, T. Menzies, N. Siegmund, and S. Apel, Finding Faster Configurations Using Flash, IEEE Transact. on Software Engineering, 2018.

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.

J. Oh, P. Gazzillo, and D. S. Batory, 2019. t-Wise Coverage by Uniform Sampling, Proceedings of the 23rd International Systems and Software Product Line Conference, SPLC 2019, vol.A, p.4

T. Parr, K. Turgutlu, C. Csiszar, and J. Howard, Beware Default Random Forest Importances, 2018.

J. A. Pereira, H. Martin, M. Acher, J. Jézéquel, 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, International Conference on Software Testing, Verification, and Validation, pp.1-12, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01991857

A. Porter, . Yilmaz, M. Atif, . Memon, C. Douglas et al., Skoll: A Process and Infrastructure for Distributed Continuous Quality Assurance, IEEE Transactions on Software Engineering, vol.33, pp.510-525, 2007.

R. Queiroz, T. Berger, and K. Czarnecki, Towards Predicting Feature Defects in Software Product Lines, Proceedings of the 7th International Workshop on Feature-Oriented Software Development, pp.58-62, 2016.

F. Samreen, Y. Elkhatib, M. Rowe, and G. Blair, Daleel: Simplifying Cloud Instance Selection Using Machine Learning, NOMS 2016-2016 IEEE/IFIP Network Operations and Management Symposium, pp.557-563, 2016.

A. Sarkar, J. Guo, and N. Siegmund, Cost-Efficient Sampling for Performance Prediction of Configurable Systems (T), IEEE/ACM International Conference on Automated Software Engineering (ASE), pp.342-352, 2015.

N. Siegmund, A. Grebhahn, S. Apel, and C. Kastner, Performance-Influence Models for Highly Configurable Systems, 10th Joint Meeting on Foundations of Software Engineering (ESEC/FSE, pp.284-294, 2015.

N. Siegmund, S. Sergiy, C. Kolesnikov, S. Kästner, D. S. Apel et al., Predicting Performance via Automated Feature-Interaction Detection, International Conference on Software Engineering (ICSE), pp.167-177, 2012.

N. Siegmund, M. Rosenmüller, M. Kuhlemann, C. Kästner, and G. Saake, Measuring Non-Functional Properties in Software Product Line for Product Derivation, 15th Asia-Pacific Software Engineering Conference, pp.187-194, 2008.

W. George, . Snedecor, G. Witiiam, and . Cochran, Statistical Methods, 8thEdn. Ames: Iowa State Univ. Press Iowa, 1989.

C. Song, A. Porter, and J. Foster, iTree: Efficiently Discovering High-Coverage Configurations Using Interaction Trees, IEEE Transactions on Software Engineering, vol.40, pp.251-265, 2013.

J. Klaas, B. Stol, and . Fitzgerald, The ABC of Software Engineering Research. ACM Trans. Softw. Eng. Methodol, vol.27, issue.11, p.51, 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, M. Acher, G. Perrouin, B. Biggio, J. Jézéquel et al., Towards quality assurance of software product lines with adversarial configurations, 23rd International Systems and Software Product Line Conference, SPLC. ACM, vol.38, p.12, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02287616

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

C. Thornton, F. Hutter, H. Holger, K. Hoos, and . Leyton-brown, Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms, Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.847-855, 2013.

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. Guo, and K. Czarnecki, Empirical Comparison of Regression Methods for Variability-Aware Performance Prediction, SPLC'15, 2015.

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.

A. Vargha, D. Harold, and . Delaney, A Critique and Improvement of the CL Common Language Effect Size Statistics of McGraw and Wong, Journal of Educational and Behavioral Statistics, vol.25, pp.101-132, 2000.

M. Varshosaz, M. Al-hajjaji, T. Thüm, and T. Runge, A Classification of Product Sampling for Software Product Lines, International Systems and Software Product Line Conference (SPLC, pp.1-13, 2018.

M. Weckesser, R. Kluge, M. Pfannemüller, M. Matthé, A. Schürr et al., Optimal Reconfiguration of Dynamic Software Product Lines Based on Performance-Influence Models, International Systems and Software Product Line Conference (SPLC), pp.98-109, 2018.

D. Westermann, J. Happe, R. Krebs, and R. Farahbod, Automated Inference of Goal-Oriented Performance Prediction Functions, IEEE/ACM International Conference on Automated Software Engineering (ASE), 2012.

C. Yilmaz, B. Myra, A. Cohen, and . Porter, Covering Arrays for Efficient Fault Characterization in Complex Configuration Spaces, IEEE Transactions on Software Engineering, vol.32, issue.1, pp.20-34, 2006.

Y. Zhang, J. Guo, E. Blais, K. Czarnecki, and H. Yu, A Mathematical Model of Performance-Relevant Feature Interactions, International Systems and Software Product Line Conference (SPLC), pp.25-34, 2016.

W. Zheng, R. Bianchini, and T. D. Nguyen, Automatic Configuration of Internet Services, ACM SIGOPS Operating Systems Review, vol.41, pp.219-229, 2007.

, Sampling Effect on Performance Prediction of Configurable Systems: A Case Study (Artifact)