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Bibliographic Details
Main Authors: Menzies, Tim, Ganguly, Kishan Kumar
Format: Preprint
Published: 2026
Subjects:
Online Access:https://arxiv.org/abs/2603.10478
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author Menzies, Tim
Ganguly, Kishan Kumar
author_facet Menzies, Tim
Ganguly, Kishan Kumar
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2603_10478
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle From Verification to Herding: Exploiting Software's Sparsity of Influence
Menzies, Tim
Ganguly, Kishan Kumar
Software Engineering
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.
title From Verification to Herding: Exploiting Software's Sparsity of Influence
topic Software Engineering
url https://arxiv.org/abs/2603.10478