E-SPARK: Automated Generation of Provably Correct Code from Formally Verified Designs

An approach to generating provably correct sequential code from formallydeveloped algorithmic designs is presented. Given an algorithm modelledin the Event-B formalism, we automatically translate the design into the SPARKprogramming language. Our translation builds upon Abrial’s approach to the developmentof sequential programs from Event-B models. However, as well as generatingcode, our approach also automatically generates code level specifications, i.e.SPARK pre- and post-conditions, along with loop invariants. In terms of the SPARKproof tools, having the loop invariants increases verification automation. A prototype,known as E-SPARK, has been implemented as a plugin for the Rodin Platform(Event-B toolkit), and tested on a range of examples, i.e. searching, sorting andnumeric calculations

Abstract State Machines 1988-1998: Commented ASM Bibliography

An annotated bibliography of papers which deal with or use Abstract State Machines (ASMs), as of January 1998.Comment: Also maintained as a BibTeX file at http://www.eecs.umich.edu/gasm

Provably safe systems: the only path to controllable AGI

We describe a path to humanity safely thriving with powerful Artificial General Intelligences (AGIs) by building them to provably satisfy human-specified requirements. We argue that this will soon be technically feasible using advanced AI for formal verification and mechanistic interpretability. We further argue that it is the only path which guarantees safe controlled AGI. We end with a list of challenge problems whose solution would contribute to this positive outcome and invite readers to join in this work.Comment: 17 page

Program debugging and validation using semantic approximations and partial specifications

The technique of Abstract Interpretation [11] has allowed the development of sophisticated program analyses which are provably correct and practical. The semantic approximations produced by such analyses have been traditionally applied to optimization during program compilation. However, recently, novel and promising applications of semantic approximations have been proposed in the more general context of program validation and debugging [3,9,7]

Sampling for Bayesian program learning

Towards learning programs from data, we introduce the problem of sampling programs from posterior distributions conditioned on that data. Within this setting, we propose an algorithm that uses a symbolic solver to efficiently sample programs. The proposal combines constraint-based program synthesis with sampling via random parity constraints. We give theoretical guarantees on how well the samples approximate the true posterior, and have empirical results showing the algorithm is efficient in practice, evaluating our approach on 22 program learning problems in the domains of text editing and computer-aided programming.National Science Foundation (U.S.) (Award NSF-1161775)United States. Air Force Office of Scientific Research (Award FA9550-16-1-0012