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Performance-influence models can help stakeholders understand how and where configuration options and their interactions influence the performance of a system.
Performance modeling and evaluation of distributed component-based systems using queueing Petri Nets
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Design and Analysis of Experiments
D. C. Montgomery · 2006
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Statistically rigorous Java performance evaluation
A. Georges, D. Buytaert, and L. Eeckhout · 2007
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The Palladio component model for model-driven performance prediction
S. Becker, H. Koziolek, and R. Reussner · 2009
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Measure Java performance – sampling or instrumentation?, Jan. 2011
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A survey of combinatorial testing
C. Nie and H. Leung · 2011
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Feature-Oriented Software Product Lines: Concepts and Implementation
S. Apel, D. Batory, C. Kästner, and G. Saake · 2013
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A learning-based framework for engineering feature-oriented self-adaptive software systems
N. Esfahani, A. Elkhodary, and S. Malek · 2013
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Performance Modeling and Design of Computer Systems: Queueing Theory in Action
M. Harchol-Balter · 2013
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Introduction to Combinatorial Testing
D. R. Kuhn, R. N. Kacker, and Y. Lei · 2013
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Phosphor: Illuminating dynamic data flow in commodity JVMs
J. Bell and G. Kaiser · 2014
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Explaining prediction models and individual predictions with feature contributions
E. Štrumbelj and I. Kononenko · 2014
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A classification and survey of analysis strategies for software product lines
T. Thüm, S. Apel, C. Kästner, I. Schaefer, and G. Saake · 2014
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Cere: Llvm-based codelet extractor and replayer for piecewise benchmarking and optimization
P. D. O. Castro, C. Akel, E. Petit, M. Popov, and W. Jalby · 2015
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Performance modeling of automated systems
N. Viswanadham and Y. Narahari · 2015
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Optimal Minimisation of Pairwise-covering Test Configurations Using Constraint Programming
A. Hervieu, D. Marijan, A. Gotlieb, and B. Baudry · 2016
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Towards a rigorous science of interpretable machine learning
Tracking load-time configuration options
M. Lillack, C. Kästner, and E. Bodden · 2018
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Time-space efficient regression testing for configurable systems
S. Souto and M. d’Amorim · 2018
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Pinpointing and repairing performance bottlenecks in concurrent programs
T. Yu and M. Pradel · 2018
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Predicting performance of software configurations: There is no silver bullet, 2019
A. Grebhahn, N. Siegmund, and S. Apel · 2019
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Interpretable Machine Learning
C. Molnar · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
C. Rudin · 2019
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F. Doshi-Velez and B. Kim · 2017
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Data-efficient performance learning for configurable systems
J. Guo, D. Yang, N. Siegmund, S. Apel, A. Sarkar, P. Valov, K. Czarnecki, A. Wasowski, and H. Yu · 2017
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A unified approach to interpreting model predictions
S. M. Lundberg and S.-I. Lee · 2017
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Using bad learners to find good configurations
V. Nair, T. Menzies, N. Siegmund, and S. Apel · 2017
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Test them all, is it worth it? assessing configuration sampling on the JHipster web development stack
A. Halin, A. Nuttinck, M. Acher, X. Devroey, G. Perrouin, and B. Baudry · 2018
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Tradeoffs in modeling performance of highly configurable software systems
S. Kolesnikov, N. Siegmund, C. Kästner, A. Grebhahn, and S. Apel · 2018
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Incling: Efficient product-line testing using incremental pairwise sampling
M. Al-Hajjaji, S. Krieter, T. Thüm, M. Lochau, and G. Saake
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The interplay of sampling and machine learning for software performance prediction
C. Kaltenecker, A. Grebhahn, N. Siegmund, and S. Apel · 2020
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CADET: A systematic method for debugging misconfigurations using counterfactual reasoning, 2020
R. Krishna, M. S. Iqbal, M. A. Javidian, B. Ray, and P. Jamshidi · 2020
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Configcrusher: Towards white-box performance analysis for configurable systems
M. Velez, P. Jamshidi, F. Sattler, N. Siegmund, S. Apel, and C. Kästner · 2020
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White-box analysis over machine learning: Modeling performance of configurable systems - Supplementary Material - https://bit.ly/3bbbgG8, 2021
M. Velez, P. Jamshidi, N. Siegmund, S. Apel, and C. Kästner · 2021
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