Learning to sample: exploiting similarities across environments to learn performance models for configurable systems

Article Details
Title:	Learning to sample: exploiting similarities across environments to learn performance models for configurable systems
Article URLs:	https://doi.org/10.1145/3236024.3236074
Alternative Article URLs:
Authors:	Pooyan Jamshidi	University of South Carolina
	Miguel Velez	Carnegie Mellon University
	Christian Kästner	Carnegie Mellon University
	Norbert Siegmund	Bauhaus-University
Sharing:	Unknown
Verification:	Authors have not verified information
Artifact Evaluation Badge:	none
Artifact URLs:	https://github.com/cmu-mars/model-learner/tree/tutorial https://github.com/pooyanjamshidi/GenPerf https://github.com/pooyanjamshidi/deeparch-xplorer https://github.com/pooyanjamshidi/xgboost-xplorer https://github.com/pooyanjamshidi/storm-xplorer https://github.com/pooyanjamshidi/ase17 https://github.com/pooyanjamshidi/L2S
Artifact Correspondence Email Addresses:
NSF Award Numbers:	1318808, 1552944, 1717022
DBLP Key:	conf/sigsoft/JamshidiVKS18
Author Comments:

ACM Foundations of Software Engineering, FSE 2018