This article describes the confluence framework, a novel framework for proving and disproving confluence using a divide-and-conquer modular strategy, and its implementation in CONFident. Using this approach, we are able to automatically prove and disprove confluence of Generalized Term Rewriting Systems, where (i) only selected arguments of function symbols can be rewritten and (ii) a rather general class of conditional rules can be used. This includes, as particular cases, several variants of rewrite systems such as (context-sensitive) term rewriting systems, string rewriting systems, and (context-sensitive) conditional term rewriting systems. The divide-and-conquer modular strategy allows us to combine in a proof tree different techniques for proving confluence, including modular decompositions, checking joinability of (conditional) critical and variable pairs, transformations, etc., and auxiliary tasks required by them, e.g., joinability of terms, joinability of conditional pairs, etc.

Context-sensitive rewriting is a restriction of term rewriting where reductions are allowed on specific arguments of function symbols only, and then in particular positions of terms. Confluence is an abstract property of reduction relations guaranteeing that two diverging reduction sequences can always be joined into a common reduct. In this paper we investigate confluence of context-sensitive rewriting and present some novel results. In particular, a characterization of local confluence of context-sensitive rewriting as the joinability of an extended class of critical pairs which we introduce here. We also show that the treatment of joinability of critical pairs using theorem proving and solving feasibility problems is useful to automatically prove and disprove confluence of context-sensitive rewriting. Our techniques have been implemented in a new tool, CONFident. We show by means of benchmarks the impact of the new techniques discussed in the paper.

We characterize conditional rewriting as satisfiability in a Herbrand-like model of terms where variables are also included as fresh constant symbols extending the original signature. Confluence of conditional rewriting and joinability of conditional critical pairs is characterized similarly. Joinability of critical pairs is then translated into combinations of (in)feasibility problems which can be efficiently handled by a number of automatic tools. This permits a more efficient use of standard results for proving confluence of conditional term rewriting systems, most of them relying on auxiliary proofs of joinability of conditional critical pairs, perhaps with additional syntactical and (operational) termination requirements on the system. Our approach has been implemented in a new system: CONFident . Its ability to (dis)prove confluence of conditional term rewriting systems is witnessed by means of some benchmarks comparing our tool with existing tools for similar purposes.

Abstract In order to automatically infer the resource consumption of programs, analyzers track how data sizes change along program’s execution. Typically, analyzers measure the sizes of data by applying norms which are mappings from data to natural numbers that represent the sizes of the corresponding data. When norms are defined by taking type information into account, they are named typed-norms. This article presents a transformational approach to resource analysis with typed-norms that are inferred by a data-flow analysis. The analysis is based on a transformation of the program into an intermediate abstract program in which each variable is abstracted with respect to all considered norms which are valid for its type. We also present the data-flow analysis to automatically infer the required, useful, typed-norms from programs. Our analysis is formalized on a simple rule-based representation to which programs written in different programming paradigms (e.g., functional, logic, and imperative) can be automatically translated. Experimental results on standard benchmarks used by other type-based analyzers show that our approach is both efficient and accurate in practice.