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This paper introduces "herding," a testing approach that treats software testing as a model-free search task, exploiting the "Sparsity of Influence" principle where a few variables control large software state spaces. They propose EZR (Efficient Zero-knowledge Ranker), a stochastic learner, to identify these key controlling variables directly through sampling. Empirical results across numerous tasks show that EZR achieves 90% of peak performance using only 32 samples, offering a lightweight alternative to traditional, resource-intensive software verification methods.
Forget exhaustive verification: a surprisingly small number of tests can steer complex software systems towards desired goals by exploiting the "Sparsity of Influence".
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.
