VaryLATEX
Author:
Affiliation:
1. Univ Rennes, Inria, CNRS, IRISA, Rennes, France
2. University of Sevilla, Sevilla, Spain
3. Sorbonne University UPMC, Paris, France
Funder
ANR-17-CE25-0010-01
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3168365.3168372
Reference19 articles.
1. Product lines can jeopardize their trade secrets
2. FAMILIAR: A domain-specific language for large scale management of feature models
3. Automated analysis of feature models 20 years later: A literature review
4. Modeling and Building Software Product Lines with Pure::Variants
5. Jianmei Guo Krzysztof Czarnecki Sven Apel Norbert Siegmund and Andrzej Wasowski. 2013. Variability-aware performance prediction: A statistical learning approach. In ASE. 10.1109/ASE.2013.6693089 Jianmei Guo Krzysztof Czarnecki Sven Apel Norbert Siegmund and Andrzej Wasowski. 2013. Variability-aware performance prediction: A statistical learning approach. In ASE. 10.1109/ASE.2013.6693089
Cited by 17 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Input sensitivity on the performance of configurable systems an empirical study;Journal of Systems and Software;2023-07
2. Specialization of Run-time Configuration Space at Compile-time: An Exploratory Study;Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing;2023-03-27
3. Explicit or Implicit? On Feature Engineering for ML-based Variability-intensive Systems;Proceedings of the 17th International Working Conference on Variability Modelling of Software-Intensive Systems;2023-01-25
4. Conjunctive Query Based Constraint Solving for Feature Model Configuration;The 12th Conference on Information Technology and Its Applications;2023
5. Machine Learning for Feature Constraints Discovery;Handbook of Re-Engineering Software Intensive Systems into Software Product Lines;2022-07-05
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