Zoom, Don't Wander: Why Regional Search Outperforms Pareto Reasoning and Global Optimization in Budget-Constrained SBSE

2026-05-10

AI Summary

This work addresses the inefficiency and suboptimal performance of traditional global search and explicit Pareto front construction in budget-constrained search-based software engineering (SBSE). The authors propose a “focus” strategy that employs an Extremely frugal and greedy Zooming-in on Regions (EZR) algorithm to rapidly identify compact, high-quality regions in the decision space, deliberately avoiding explicit Pareto front approximation and thereby challenging conventional SBSE paradigms. By integrating localized search, EZR replaces complex Bayesian optimization and multi-objective evolutionary algorithms, achieving up to three orders of magnitude improvement in computational efficiency. It matches or outperforms state-of-the-art methods on 84–89% of datasets, maintains superiority in 79–81% of tasks even when the evaluation budget is reduced to one-fifth, and achieves competitive or better results in terms of IGD and hypervolume metrics, while also enhancing model interpretability.

Abstract

Traditional Search-Based Software Engineering (SBSE) assumes global search and full Pareto exploration are essential. We offer the following negative result based on a study of over 100 Software Engineering (SE) optimization tasks: "zooming" into promising regions is far more effective than Pareto and global exploration under constrained evaluation budgets. Our minimal greedy zoom method, EZR, runs three orders of magnitude faster than Pareto and global Bayesian methods, achieving higher statistical ranks and winning or tying in 84-89\% of datasets on equal budget. Even at one-fifth the evaluation budget, EZR wins or ties in 79-81\% of datasets. Surprisingly, despite never explicitly seeking a frontier, EZR matches or outperforms Pareto methods on their own coverage metrics (IGD, HV) at equal budgets. The explanation for this widespread failure is structural: across the datasets studied, Pareto-optimal solutions form a tiny, tight island concentrated in a compact region of decision space. Methods that wander waste their budgets outside this island. Beyond efficiency, zooming yields small, interpretable models, thus addressing concerns about black-box AI. By replacing global wandering with greedy zooming, we make SBSE much faster, more explicable, and hence accessible to a wider audience. SBSE practitioners and researchers should zoom, not wander.

Problem

• Search-Based Software Engineering
• Pareto optimization
• budget-constrained optimization
• global search
• regional search

Innovation

• regional search
• greedy zooming
• budget-constrained optimization
• Pareto efficiency
• interpretable SBSE