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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.10478 |
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| _version_ | 1866917332809416704 |
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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 |