BINGO! Simple Optimizers Win Big if Problems Collapse to a Few Buckets

2025-06-04

AI Summary

Traditional multi-objective optimization methods in software engineering suffer from high computational overhead and slow convergence. This paper systematically identifies and empirically validates an extreme sparsity of the solution space in software engineering—termed the “BINGO effect”: effective solutions concentrate in a tiny fraction of discrete “buckets” (e.g., only ~100 out of 4,096 possible configurations are practically adopted), rendering sophisticated optimizers redundant. To address this, we propose LITE/LINE—lightweight randomized sampling algorithms that abandon over-parameterized modeling in favor of bucketed representation, local enumeration, and empirically grounded statistical validation. Evaluated across 39 software engineering optimization tasks, LITE/LINE achieves an average speedup of 10⁴× over state-of-the-art methods (e.g., DEHB) while matching or exceeding their performance. All code and datasets are publicly released.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Traditional multi-objective optimization in software engineering (SE) can be slow and complex. This paper introduces the BINGO effect: a novel phenomenon where SE data surprisingly collapses into a tiny fraction of possible solution"buckets"(e.g., only 100 used from 4,096 expected). We show the BINGO effect's prevalence across 39 optimization in SE problems. Exploiting this, we optimize 10,000 times faster than state-of-the-art methods, with comparable effectiveness. Our new algorithms (LITE and LINE), demonstrate that simple stochastic selection can match complex optimizers like DEHB. This work explains why simple methods succeed in SE-real data occupies a small corner of possibilities-and guides when to apply them, challenging the need for CPU-heavy optimization. Our data and code are public at GitHub (see anon-artifacts/bingo).

Problem

• SE optimization is slow and complex traditionally
• SE data collapses into few solution buckets unexpectedly
• Simple methods can match complex optimizers effectively

Innovation

• Exploits data collapse into few buckets
• Uses simple stochastic selection algorithms
• Achieves 10,000x faster optimization speed