All versus one: An empirical comparison on retrained and incremental machine learning for modeling performance of adaptable software
Given the ever-increasing complexity of adaptable software systems and their commonly hidden internal information (e.g., software runs in the public cloud), machine learning based performance modeling has gained momentum for evaluating, understanding and predicting software performance, which facili...
Saved in:
| Main Author: | |
|---|---|
| Format: | Default Conference proceeding |
| Published: |
2019
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/2134/9876320.v1 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|