EZR.py shows that a compact, readable Python toolkit can match or exceed state-of-the-art tools like SHAP, LIME, SMAC3, and FASTREAD on over 120 tabular SE tasks while running 500 times faster and using far less labeled data.
Moot: a repository of many multi-objective optimization tasks
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
dataset 1
citation-polarity summary
fields
cs.SE 2years
2026 2verdicts
UNVERDICTED 2roles
dataset 1polarities
use dataset 1representative citing papers
A minimal greedy regional zoom method outperforms Pareto and global Bayesian optimization in budget-constrained SBSE, winning or tying in 84-89% of cases at equal budget and even at one-fifth budget, because optimal solutions cluster in a compact decision-space island.
citing papers explorer
-
Can AI be Easy? Lessons Learned from the EZR.py Toolkit
EZR.py shows that a compact, readable Python toolkit can match or exceed state-of-the-art tools like SHAP, LIME, SMAC3, and FASTREAD on over 120 tabular SE tasks while running 500 times faster and using far less labeled data.
-
Zoom, Don't Wander: Why Regional Search Outperforms Pareto Reasoning and Global Optimization in Budget-Constrained SBSE
A minimal greedy regional zoom method outperforms Pareto and global Bayesian optimization in budget-constrained SBSE, winning or tying in 84-89% of cases at equal budget and even at one-fifth budget, because optimal solutions cluster in a compact decision-space island.