From Verification to Herding: Exploiting Software's Sparsity of Influence

TL;DR

This paper proposes a novel approach to software testing called herding, which leverages the sparsity of influence in large software systems to efficiently steer systems toward goals using a lightweight stochastic learner, EZR.

Contribution

It introduces EZR, a model-free, sampling-based method that exploits influence sparsity to improve software testing efficiency and effectiveness.

Findings

EZR achieves 90% of peak results with only 32 samples.

EZR replaces heavy solvers with light sampling methods.

The approach is effective across dozens of testing tasks.

Abstract

Software verification is now costly, taking over half the project effort while failing on modern complex systems. We hence propose a shift from verification and modeling to herding: treating testing as a model-free search task that steers systems toward target goals. This exploits the "Sparsity of Influence" -the fact that, often, large software state spaces are ruled by just a few variables, We introduce EZR (Efficient Zero-knowledge Ranker), a stochastic learner that finds these controllers directly. Across dozens of tasks, EZR achieved 90% of peak results with only 32 samples, replacing heavy solvers with light sampling.