Probabilistic Output Analyses for Deterministic Programs: Reusing Existing Non-probabilistic Analyses

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We consider reusing established non-probabilistic output analyses (either forward or backwards) thatyield over-approximations of a program’s pre-image or image relation, e.g., interval analyses. Weassume a probability measure over the program input and present two techniques (one for forward andone for backward analyses) that both derive upper and lower probability bounds for the output events.We demonstrate the most involved technique, namely the forward technique, for two examples andcompare their results to a cutting-edge probabilistic output analysis.
TitelProceedings 16th Workshop on Quantitative Aspects of Programming Languages and Systems Prague, Czech Republic, 7th April 2019
RedaktørerAlessandro Aldini, Herbert Wiklicky
Antal sider15
ForlagOpen Publishing Association
Publikationsdato20 jan. 2020
StatusUdgivet - 20 jan. 2020
Begivenhed16th Workshop on Quantitative Aspects of Programming Languages and Systems - Prague, Tjekkiet
Varighed: 7 apr. 20197 apr. 2019
Konferencens nummer: 16


Workshop16th Workshop on Quantitative Aspects of Programming Languages and Systems
AndetQuantitative aspects of computation refer to the use of physical quantities (time, bandwidth, etc.) as well as mathematical quantities (for example, probabilities) for the characterisation of the behaviour and for determining the properties of systems. Such quantities play a central role in defining both the model of systems (architecture, language design, semantics) and the methodologies and tools for the analysis and verification of system properties. The aim of the QAPL workshop series is to discuss the explicit use of time and probability and general quantities either directly in the model or as a tool for the analysis or synthesis of systems. The 16th edition of QAPL will also focus on discussing the developments, challenges and results in this area covered by our workshop in its nearly 20-year history.
NavnElectronic Proceedings in Theoretical Computer Science

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