Abstract This article gives an overview of automatic amortized resource analysis (AARA), a technique for inferring symbolic resource bounds for programs at compile time. AARA has been introduced by Hofmann and Jost in 2003 as a type system for deriving linear worst-case bounds on the heap-space consumption of first-order functional programs with eager evaluation strategy. Since then AARA has been the subject of dozens of research articles, which extended the analysis to different resource metrics, other evaluation strategies, non-linear bounds, and additional language features. All these works preserved the defining characteristics of the original paper: local inference rules, which reduce bound inference to numeric (usually linear) optimization; a soundness proof with respect to an operational cost semantics; and the support of amortized analysis with the potential method.

Linear tree constraints are given by pointwise linear inequalities between infinite trees labeled with nonnegative rational numbers. Satisfiablity of such constraints is at least as hard as solving the Skolem-Mahler-Lech Problem. We provide an interesting subcase, for which we prove that satisfiablity is decidable. Our decision procedure is based on intricate arguments using automata and combinatorics of words. Our subcase allows to construct an inference mechanism for resource bounds of object oriented Java-like programs: actual resource bounds can be read off from solutions of tree constraints. So far, only the case of degenerated tree constraints (i.e. lists) was known to be decidable which, however, is insufficient to generally solve the given resource analysis problem. The present paper therefore provides a generalisation to trees of higher degree in order to cover the entire range of constraints encountered by resource analysis.

This paper describes the first successful attempt, of which we are aware, to define an automatic, type-based static analysis of resource bounds for lazy functional programs. Our analysis uses the automatic amortisation approach developed by Hofmann and Jost, which was previously restricted to eager evaluation. In this paper, we extend this work to a lazy setting by capturing the costs of unevaluated expressions in type annotations and by amortising the payment of these costs using a notion of lazy potential. We present our analysis as a proof system for predicting heap allocations of a minimal functional language (including higher-order functions and recursive data types) and define a formal cost model based on Launchbury's natural semantics for lazy evaluation. We prove the soundness of our analysis with respect to the cost model. Our approach is illustrated by a number of representative and non-trivial examples that have been analysed using a prototype implementation of our analysis.

We describe a new automatic static analysis for determining upper-bound functions on the use of quantitative resources for strict, higher-order, polymorphic, recursive programs dealing with possibly-aliased data. Our analysis is a variant of Tarjan's manual amortised cost analysis technique. We use a type-based approach, exploiting linearity to allow inference, and place a new emphasis on the number of references to a data object. The bounds we infer depend on the sizes of the various inputs to a program. They thus expose the impact of specific inputs on the overall cost behaviour.

We show how to efficiently obtain linear a priori bounds on the heap space consumption of first-order functional programs.The analysis takes space reuse by explicit deallocation into account and also furnishes an upper bound on the heap usage in the presence of garbage collection. It covers a wide variety of examples including, for instance, the familiar sorting algorithms for lists, including quicksort.The analysis relies on a type system with resource annotations. Linear programming (LP) is used to automatically infer derivations in this enriched type system.We also show that integral solutions to the linear programs derived correspond to programs that can be evaluated without any operating system support for memory management. The particular integer linear programs arising in this way are shown to be feasibly solvable under mild assumptions.