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Using machine learning to predict the code size impact of d..:

, In: Proceedings of the 18th ACM SIGPLAN International Conference on Managed Programming Languages and Runtimes,

Mosaner, Raphael ; Leopoldseder, David ; Stadler, Lukas. - p. 127-135 , 2021

Link: https://dl.acm.org/doi/10.1145/3475738.3480943

RT 
T1 <cTitle>Proceedings of the 18th ACM SIGPLAN International Conference on Managed Programming Languages and Runtimes</cTitle>: T1 <Title>Using machine learning to predict the code size impact of duplication heuristics in a dynamic compiler</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-3480943&Exemplar=1&LAN=DE
				
A1 Mosaner, Raphael
A1 Leopoldseder, David
A1 Stadler, Lukas
A1 Mössenböck, Hanspeter
PB ACM
YR 2021
K1 Code Duplication
K1 Dynamic Compiler
K1 Heuristics
K1 Machine Learning
K1 Neural Networks
K1 Optimization
K1 Regression
K1 Software and its engineering
K1 Software notations and tools
K1 Compilers
K1 Just-in-time compilers
K1 Computing methodologies
K1 Machine learning
K1 Machine learning approaches
K1 Neural networks
K1 Dynamic compilers
K1 Learning paradigms
K1 Supervised learning
K1 Supervised learning by regression
K1 General and reference
K1 Cross-computing tools and techniques
K1 Empirical studies
K1 Performance
SP 127
OP 135
LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3475738.3480943
DO https://dl.acm.org/doi/10.1145/3475738.3480943
SF ELIB - SuUB Bremen

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