, TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, 2015.
42 variability bugs in the linux kernel: a qualitative analysis, ACM/IEEE International Conference on Automated Software Engineering, ASE '14, pp.421-432, 2014. ,
, Variability Bugs in Highly Configurable Systems: A Qualitative Analysis, vol.26, 2018.
The Anatomy of a Sales Configurator: An Empirical Study of 111 Cases, Advanced Information Systems Engineering -25th International Conference, pp.162-177, 2013. ,
Learning From Thousands of Build Failures of Linux Kernel Configurations, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-02147012
An empirical study of unspecified dependencies in make-based build systems, Empirical Software Engineering, vol.22, pp.3117-3148, 2017. ,
Random forests, Machine learning, vol.45, pp.5-32, 2001. ,
API design for machine learning software: experiences from the scikit-learn project, ECML PKDD Workshop: Languages for Data Mining and Machine Learning, pp.108-122, 2013. ,
URL : https://hal.archives-ouvertes.fr/hal-00856511
On Parallel Scalable Uniform SAT Witness Generation, Tools and Algorithms for the Construction and Analysis of Systems TACAS'15 2015, pp.304-319, 2015. ,
A scalable and nearly uniform generator of SAT witnesses, International Conference on Computer Aided Verification, pp.608-623, 2013. ,
Analysing the Linux kernel feature model changes using FMDiff. Software and System Modeling, vol.16, pp.55-76, 2017. ,
Collinearity: a review of methods to deal with it and a simulation study evaluating their performance, Ecography, vol.36, pp.27-46, 2013. ,
Applied regression analysis, vol.326, 1998. ,
Learning non-deterministic impact models for adaptation, Proceedings of the 13th International Conference on Software Engineering for Adaptive and Self-Managing Systems, pp.196-205, 2018. ,
Efficient sampling of SAT solutions for testing, Proceedings of the 40th International Conference on Software Engineering, ICSE 2018, pp.549-559, 2018. ,
Performance-based selection of software and hardware features under parameter uncertainty, Proceedings of the 10th international ACM Sigsoft conference on Quality of software architectures, pp.23-32, 2014. ,
All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously, 2018. ,
Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems, 2017. ,
A large-scale study of the impact of feature selection techniques on defect classification models, 2017 IEEE/ACM 14th International Conference on Mining Software Repositories (MSR), pp.146-157, 2017. ,
Variability-aware performance prediction: A statistical learning approach, pp.301-311, 2013. ,
Dataefficient performance learning for configurable systems, Empirical Software Engineering, pp.1-42, 2017. ,
DeepPerf: performance prediction for configurable software with deep sparse neural network, Proceedings of the 41st International Conference on Software Engineering, ICSE 2019, pp.1095-1106, 2019. ,
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
Effective Program Debloating via Reinforcement Learning, Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security (CCS '18, pp.380-394, 2018. ,
, A flexible Python 2/3 Kconfig implementation and library, pp.2019-2024
, Linux Kernel Tinification. last access, 2019.
Supporting multiple perspectives in feature-based configuration, Software and System Modeling, vol.12, pp.641-663, 2013. ,
URL : https://hal.archives-ouvertes.fr/hal-00718144
A user survey of configuration challenges in Linux and eCos, Sixth International Workshop on Variability Modelling of Software-Intensive Systems, pp.149-155, 2012. ,
, Machine Learning Meets Quantitative Planning: Enabling SelfAdaptation in Autonomous Robots, 2019.
Transfer learning for performance modeling of configurable systems: an exploratory analysis, pp.497-508, 2017. ,
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. ,
Configurations Everywhere: Implications for Testing and Debugging in Practice, Companion Proceedings of the 36th International Conference on Software Engineering (ICSE Companion, pp.215-224, 2014. ,
Distance-Based Sampling of Software Configuration Spaces, Proceedings of the IEEE/ACM International Conference on Software Engineering (ICSE), 2019. ,
On the relation of external and internal feature interactions: A case study, 2017. ,
, Tradeoffs in modeling performance of highly configurable software systems. Software & Systems Modeling, vol.18, pp.2265-2283, 2019.
The impact of feature reduction techniques on defect prediction models, Empirical Software Engineering, pp.1-39, 2019. ,
Configuration-Driven Software Debloating, Proceedings of the 12th European Workshop on Systems Security (EuroSec '19), vol.6, 2019. ,
Attack Surface Reduction for Commodity OS Kernels: Trimmed Garden Plants May Attract Less Bugs, Proceedings of the Fourth European Workshop on System Security (EUROSEC '11, 2011. ,
JMake: Dependable Compilation for Kernel Janitors, 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, pp.357-366, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01555711
Coccinelle: 10 Years of Automated Evolution in the Linux Kernel, 2018 USENIX Annual Technical Conference, USENIX ATC 2018, pp.601-614, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01853271
Multifaceted automated analyses for variability-intensive embedded systems, Proceedings of the 41st International Conference on Software Engineering, pp.854-865, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-02061251
A comparison of 10 sampling algorithms for configurable systems, Proceedings of the 38th International Conference on Software Engineering -ICSE '16, pp.643-654, 2016. ,
On essential configuration complexity: measuring interactions in highly-configurable systems, Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering, ASE 2016, pp.483-494, 2016. ,
A Quantitative Analysis of Variability Warnings in Linux, Proceedings of the Tenth International Workshop on Variability Modelling of Software-intensive Systems (VaMoS '16), pp.3-8, 2016. ,
A quantitative analysis of variability warnings in linux, Proceedings of the Tenth International Workshop on Variability Modelling of Software-intensive Systems, pp.3-8, 2016. ,
, Interpretable Machine Learning, 2019.
,
, Where Do Configuration Constraints Stem From? An Extraction Approach and an Empirical Study, IEEE Trans. Software Eng
Linux Variability Anomalies: What Causes Them and How Do They Get Fixed, Proceedings of the 10th Working Conference on Mining Software Repositories (MSR '13), pp.111-120, 2013. ,
Using bad learners to find good configurations, Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering, pp.257-267, 2017. ,
Finding faster configurations using flash, IEEE Transactions on Software Engineering, 2018. ,
Transfer defect learning, 35th International Conference on Software Engineering (ICSE), pp.382-391, 2013. ,
An interleaving approach to combinatorial testing and failure-inducing interaction identification, IEEE Transactions on Software Engineering, pp.1-1, 2018. ,
, BoF: Embedded Linux Size. Embedded Linux Conference, 2018.
Beware Default Random Forest Importances, 2018. ,
A Study of Feature Scattering in the Linux Kernel, IEEE Transactions on Software Engineering, pp.1-1, 2018. ,
A Study of Feature Scattering in the Linux Kernel, IEEE Transactions on Software Engineering, 2018. ,
, Learning Software Configuration Spaces: A Systematic Literature Review, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02148791
Uniform Sampling of SAT Solutions for Configurable Systems: Are We There Yet, ICST 2019 -12th International Conference on Software Testing, Verification, and Validation, pp.1-12, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-01991857
Towards scalable configuration testing in variable software, ACM SIG-PLAN Notices, vol.52, pp.156-167, 2016. ,
CostEfficient Sampling for Performance Prediction of Configurable Systems (T), ASE'15, 2015. ,
Cost-efficient sampling for performance prediction of configurable systems (t), pp.342-352, 2015. ,
On crossstack configuration errors, Proceedings of the 39th International Conference on Software Engineering, pp.255-265, 2017. ,
Software Configuration Engineering in Practice: Interviews, Survey, and Systematic Literature Review, IEEE Transactions on Software Engineering, 2018. ,
Performance-influence Models for Highly Configurable Systems, Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering, pp.284-294, 2015. ,
,
, Performance-Influence Models for Highly Configurable Systems, ESEC/FSE'15
Attributed variability models: outside the comfort zone, Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering, pp.268-278, 2017. ,
Approaching non-functional properties of software product lines: Learning from products, Software Engineering Conference (APSEC), 2010 17th Asia Pacific, pp.147-155, 2010. ,
Statistical analysis of gene expression microarray data, 2003. ,
, The ABC of Software Engineering Research. ACM Trans. Softw. Eng. Methodol, vol.27, p.11, 2018.
Learning Contextual-Variability Models, IEEE Software, vol.34, pp.64-70, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01659137
Using Machine Learning to Infer Constraints for Product Lines, Software Product Line Conference (SPLC), 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01323446
A Classification and Survey of Analysis Strategies for Software Product Lines, Comput. Surveys, 2014. ,
Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society: Series B (Methodological), vol.58, pp.267-288, 1996. ,
The linux edge, Commun. ACM, vol.42, pp.38-38, 1999. ,
Transferring Performance Prediction Models Across Different Hardware Platforms, Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering, pp.39-50, 2017. ,
Transferring performance prediction models across different hardware platforms, Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering, pp.39-50, 2017. ,
Automatic database management system tuning through large-scale machine learning, Proceedings of the 2017 ACM International Conference on Management of Data, pp.1009-1024, 2017. ,
Optimal reconfiguration of dynamic software product lines based on performance-influence models, Proceeedings of the 22nd International Conference on Software Product Line, pp.98-109, 2018. ,
Generating range fixes for software configuration, 34th International Conference on Software Engineering, ICSE 2012, pp.58-68, 2012. ,
Generating Range Fixes for Software Configuration, 34th International Conference on Software Engineering, 2012. ,
Range Fixes: Interactive Error Resolution for Software Configuration, IEEE Trans. Software Eng, vol.41, pp.603-619, 2015. ,
Hey, you have given me too many knobs!: understanding and dealing with over-designed configuration in system software, Proceedings of the 10th Joint Meeting on Foundations of Software Engineering, ESEC/FSE 2015, pp.307-319, 2015. ,
Covering arrays for efficient fault characterization in complex configuration spaces, IEEE Transactions on Software Engineering, vol.32, issue.1, pp.20-34, 2006. ,
A mathematical model of performance-relevant feature interactions, Proceedings of the 20th International Software Product Line Conference, pp.25-34, 2016. ,
Automatic configuration of internet services, ACM SIGOPS Operating Systems Review, vol.41, pp.219-229, 2007. ,
On the Scalability of Linux Kernel Maintainers' Work, Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering, pp.27-37, 2017. ,