A Transformational Approach to Resource Analysis with Typed-norms Inference

In order to automatically infer the resource consumption of programs, analyzers track howdata sizeschange along program's execution. Typically, analyzers measure the sizes of data by applyingnormswhich 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 namedtyped-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 anintermediate abstract programin 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.