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Found 4 papers, 4 papers with code
Deep Configuration Performance Learning: A Systematic Survey and Taxonomy
Performance is arguably the most crucial attribute that reflects the behavior of a configurable software system.
Predicting Configuration Performance in Multiple Environments with Sequential Meta-learning
Through comparing with 15 state-of-the-art models under nine systems, our extensive experimental results demonstrate that SeMPL performs considerably better on 89% of the systems with up to 99% accuracy improvement, while being data-efficient, leading to a maximum of 3. 86x speedup.
Predicting Software Performance with Divide-and-Learn
Predicting the performance of highly configurable software systems is the foundation for performance testing and quality assurance.
Does Configuration Encoding Matter in Learning Software Performance? An Empirical Study on Encoding Schemes
Learning and predicting the performance of a configurable software system helps to provide better quality assurance.
