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Inferring Expected Runtimes of Probabilistic Integer Programs Using Expected Sizes

We present a novel modular approach to infer upper bounds on the expected runtime of probabilistic integer programs automatically. To this end, it computes bounds on the runtime of program parts and on the sizes of their variables in an alternating way. To evaluate its power, we implemented our approach in a new version of our open-source tool KoAT.

preprint2021arXivOpen access
Fabian MeyerMarcel HarkJürgen Giesl
Logic in Computer Science
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Open access3 authors1 topic
Imported metadata coverageMissing code, dataset, citation and institution fields are tracked without dominating the paper.Details
Citations: 0Reviews: 0Saves: 0Code: not linkedDataset: not linkedInstitutions: 0

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Fabian MeyerMarcel HarkJürgen Giesl

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