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Abstract. Provenance techniques aim to increase the reliability of human judgments about data by making its origin and derivation process explicit. Originally motivated by the needs of scientific databases and scientific computation, provenance has also become a major issue for business and government data on the Web. However, so far provenance has been studied only in relatively restrictive settings: typically, for data stored in databases or scientific workflow systems, and processed by query or workflow languages of limited expressiveness. Long-term provenance solutions require an understanding of provenance in other settings, particularly the general-purpose programming or scripting languages that are used to glue different components such as databases, Web services and workflows together. Moreover, what is required is not only an account of mechanisms for recording provenance, but also a theory of what it means for provenance information to explain or justify a computation. In this paper, we begin to outline a such a theory of self-explaining computation. We introduce a model of provenance for a simple imperative language based on operational derivations and explore its properties.

We offer a lattice-theoretic account of dynamic slicing for π-calculus, building on prior work in the sequential setting. For any run of a concurrent program, we exhibit a Galois connection relating forward slices of the start configuration to backward slices of the end configuration. We prove that, up to lattice isomorphism, the same Galois connection arises for any causally equivalent execution, allowing an efficient concurrent implementation of slicing via a standard interleaving semantics. Our approach has been formalised in the dependently-typed language Agda.