Pedro López-García

Daniela Ferreiro, Daniel Jurjo-Rivas, Marco Ciccalè et al.

In strongly-typed languages, types are verified at compile time, while dynamically typed languages, such as Prolog, perform type consistency checks entirely at run-time. Extending dynamic languages with assertions allows expressing both classical types and more general properties, providing high expressiveness, but at the cost of run-time overhead. Abstract interpretation allows safely approximating such program properties at compile time, which has been used to reduce the number of properties that require run-time checks, while still reporting unverified properties that can guide further static analyses, testing, or domain refinement. In this work, we first study how to selectively integrate the run-time semantics of assertion properties into a multivariant, top-down, goal-directed abstract interpretation algorithm. We then show how multiple inferred calling patterns can be exploited to reduce the number of properties that must be checked at run-time, thus minimizing the overhead. Finally, we report on an implementation of our approach in the Ciao system and provide performance results supporting that better results can be obtained than with the previously reported techniques.

Louis Rustenholz, Maximiliano Klemen, Miguel Ángel Carreira-Perpiñán et al.

Automatic static cost analysis infers information about the resources used by programs without actually running them with concrete data, and presents such information as functions of input data sizes. Most of the analysis tools for logic programs (and many for other languages), as CiaoPP, are based on setting up recurrence relations representing (bounds on) the computational cost of predicates, and solving them to find closed-form functions. Such recurrence solving is a bottleneck in current tools: many of the recurrences that arise during the analysis cannot be solved with state-of-the-art solvers, including Computer Algebra Systems (CASs), so that specific methods for different classes of recurrences need to be developed. We address such a challenge by developing a novel, general approach for solving arbitrary, constrained recurrence relations, that uses machine-learning (sparse-linear and symbolic) regression techniques to guess a candidate closed-form function, and a combination of an SMT-solver and a CAS to check if it is actually a solution of the recurrence. Our prototype implementation and its experimental evaluation within the context of the CiaoPP system show quite promising results. Overall, for the considered benchmarks, our approach outperforms state-of-the-art cost analyzers and recurrence solvers, and solves recurrences that cannot be solved by them. Under consideration in Theory and Practice of Logic Programming (TPLP).