Can AI be Easy? Lessons Learned from the EZR.py Toolkit
For researchers and practitioners in tabular software-engineering optimization, this work demonstrates that small, unified toolkits can rival large libraries, challenging the assumption that complex AI requires heavy dependencies.
The authors present EZR.py, a 400-line Python toolkit that implements multiple AI algorithms and shows it matches or outperforms state-of-the-art tools (SHAP, LIME, SMAC3, FASTREAD) on 120+ tabular SE optimization tasks, running 500x faster than SMAC3 and using less data.
Much recent press claims that developers no longer need to read code. We disagree, at least within the domain of tabular software-engineering (SE) optimization tasks: rows of $x$ and $y$ values where the $y$ values are expensive to obtain. As evidence we present 400 lines of EZR.py, a Python toolkit (no heavy dependencies) that implements Naive Bayes, $k$-means clustering, classification and regression trees, simulated annealing, local search, active learning, and complementary-Bayes text-mining relevance filtering for tabular SE data. EZR was built by repeatedly reading and refactoring AI tools to simplify and unify them. The result demonstrates that many seemingly different learning algorithms are nearly the same once stripped back to their core: classical algorithms collapse to a few lines each, and a state-of-the-art active learner fits in roughly 80 lines. Tested on the 120+ tabular SE optimization tasks in the MOOT repository, these tiny tools perform as well as or better than state-of-the-art explanation tools (SHAP, LIME), the SMAC3 optimizer, and SVM-based text-mining filters (FASTREAD), while running 500$\times$ faster than SMAC3, using orders of magnitude less labelled data, and building trees from fewer than ten variables even when thousands are available. We conclude that, within the scope of tabular SE optimization, reading and refactoring code is a useful method of generating insight, and small unified toolkits can rival large libraries. EZR is available under an open-source license. Install via \textsf{pip install ezr}; example data at \textsf{github.com/timm/moot}.