A new paper introduces Model4Tune, a predictive tool designed to help practitioners select the most effective surrogate models for software configuration tuning. The research challenges the conventional focus on model accuracy, proposing a new theory based on fitness landscape analysis to assess model usefulness. Empirical studies involving up to 27,000 cases demonstrate that Model4Tune significantly outperforms random guessing in identifying optimal model-tuner pairs for unseen systems, thereby reducing the effort required for software configuration engineering. AI
IMPACT Offers a novel approach to model selection for software tuning, potentially improving efficiency in system performance engineering.
RANK_REASON The cluster contains a research paper published on arXiv detailing a new methodology and tool for software configuration tuning. [lever_c_demoted from research: ic=1 ai=0.7]
- alphaXiv
- arXiv
- CatalyzeX Code Finder for Papers
- DagsHub
- Gotit.pub
- Hugging Face
- Influence Flower
- Model4Tune
- Pengzhou Chen
- ScienceCast
- software engineering
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