Abstract
Having a precise yet sound abstraction of the inputs of numerical programs is important to analyze their behavior. For many programs, these inputs are probabilistic, but the actual distribution used is only partially known. We present a static analysis framework for reasoning about programs with inputs given as imprecise probabilities: we define a collecting semantics based on the notion of previsions and an abstract semantics based on an extension of Dempster-Shafer structures. We prove the correctness of our approach and show on some realistic examples the kind of invariants we are able to infer.
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Adje, A., Bouissou, O., Goubault-Larrecq, J., Goubault, E., Putot, S. (2014). Static Analysis of Programs with Imprecise Probabilistic Inputs. In: Cohen, E., Rybalchenko, A. (eds) Verified Software: Theories, Tools, Experiments. VSTTE 2013. Lecture Notes in Computer Science, vol 8164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54108-7_2
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DOI: https://doi.org/10.1007/978-3-642-54108-7_2
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