This article The purpose of this paper is to introduce b, a simply typed -calculus that supports type-based recursive definitions. Although heavily inspired from previous work by Giménez (Giménez 1998) and closely related to recent work by Amadio and Coupet (Amadio and Coupet-Grimal 1998), the technical machinery behind our system puts a slightly different emphasis on the interpretation of types. More precisely, we formalize the notion of type-based termination using a restricted form of type dependency (a.k.a. indexed types), as popularized by (Xi and Pfenning 1998; Xi and Pfenning 1999). This leads to a simple and intuitive system which is robust under several extensions, such as mutually inductive datatypes and mutually recursive function definitions; however, such extensions are not treated in the paper

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Abstract. We present a static analysis that infers both upper and lower bounds on the usage that a logic program makes of a set of user-definable resources. The inferred bounds will in general be functions of input data sizes. A resource in our approach is a quite general, user-defined notion which associates a basic cost function with elementary operations. The analysis then derives the related (upper- and lower-bound) resource usage functions for all predicates in the program. We also present an assertion language which is used to define both such resources and resourcerelated properties that the system can then check based on the results of the analysis. We have performed some preliminary experiments with some concrete resources such as execution steps, bytes sent or received by an application, number of files left open, number of accesses to a database, number of calls to a procedure, number of asserts/retracts, etc. Applications of our analysis include resource consumption verification and debugging (including for mobile code), resource control in parallel/distributed computing, and resource-oriented specialization. 1

Many software properties can be analysed through a relational size analysis on each function’s inputs and outputs. Such relational analysis (through a form of dependent typing) has been successfully applied to declarative programs, and to restricted imperative programs; but it has been elusive for object-based programs. The main challenge is that objects may mutate and they may be aliased. In this paper, we show how safety policies of programs can be analysed by tracking size properties of objects and be enforced by objects ’ invariants and the preconditions of methods. We propose several new ideas to allow both mutability and sharing of objects, whilst aiming for precision in our analysis. We introduce the concept of size-immutability to facilitate sharing, and also a set of alias controls to track unaliased objects whose size properties may change. We formalise our results through a set of advanced type checking rules for an object-based imperative language. We re-affirm the utility of the proposed type system by showing how a variety of software properties can be automatically verified according to size-inspired safety policies.

Partial evaluation is a program-transformation technique that automatically specializes a program with respect to user-supplied invariants. Despite successful applications in areas such as graphics, operating systems, and software engineering, partial evaluators have yet to achieve widespread use. One reason is the difficulty of adequately describing specialization opportunities. Indeed, under-specialization or over-specialization often occurs, without any direct feedback to the user as to the source of the problem. We have developed a high-level, module-based language allowing the programmer to guide the choice of both the code to specialize and the invariants to exploit during the specialization process. To ease the use of partial evaluation, the syntax of this language is similar to the declaration syntax of the target language of the partial evaluator. To provide feedback to the programmer, declarations are checked throughout the analyses performed by partial evaluation. The language has been successfully used by a signal-processing expert in the design of a specializable Forward Error Correction component. 1.

Abstract. In 2003, Hofmann and Jost introduced a type system that uses a potential-based amortized analysis to infer bounds on the resource consumption of (first-order) functional programs. This analysis has been successfully applied to many standard algorithms but is limited to bounds that are linear in the size of the input. Here we extend this system to polynomial resource bounds. An automatic amortized analysis is used to infer these bounds for functional programs without further annotations if a maximal degree for the bounding polynomials is given. The analysis is generic in the resource and can obtain good bounds on heap-space, stack-space and time usage.

Cost information can be exploited in a variety of contexts, including parallelizing compilers, autonomic GRIDs and real-time systems.

Automatic cost analysis of programs has been traditionally concentrated on a reduced number of resources such as execution steps, time, or memory. However, the increasing relevance of analysis applications such as static debugging and/or certification of user-level properties (including for mobile code) makes it interesting to develop analyses for resource notions that are actually application-dependent. This may include, for example, bytes sent or received by an application, number of files left open, number of SMSs sent or received, number of accesses to a database, money spent, energy consumption, etc. We present a fully automated analysis for inferring upper bounds on the usage that a Java bytecode program makes of a set of application programmer-definable resources. In our context, a resource is defined by programmer-provided annotations which state the basic consumption that certain program elements make of that resource. From these definitions our analysis derives functions which return an upper bound on the usage that the whole program (and individual blocks) make of that resource for any given set of input data sizes. The analysis proposed is independent of the particular resource. We also present some experimental results from a prototype implementation of the approach covering a significant set of interesting resources.

We study the problem of automatically analyzing the worst-case resource usage of procedures with several arguments. Existing automatic analyses based on amortization, or sized types bound the resource usage or result size of such a procedure by a sum of unary functions of the sizes of the arguments. In this paper we generalize this to arbitrary multivariate polynomial functions thus allowing bounds of the form mn which had to be grossly overestimated by m 2 + n 2 before. Our framework even encompasses bounds like ∑ i,j≤n mimj where the mi are the sizes of the entries of a list of length n. This allows us for the first time to derive useful resource bounds for operations on matrices that are represented as lists of lists and to considerably improve bounds on other super-linear operations on lists such as longest common subsequence and removal of duplicates from lists of lists. Furthermore, resource bounds are now closed under composition which improves accuracy of the analysis of composed programs when some or all of the components exhibit super-linear resource or size behavior. The analysis is based on a novel multivariate amortized resource analysis. We present it in form of a type system for a simple firstorder functional language with lists and trees, prove soundness, and describe automatic type inference based on linear programming. We have experimentally validated the automatic analysis on a wide range of examples from functional programming with lists and trees. The obtained bounds were compared with actual resource consumption. All bounds were asymptotically tight, and the constants were close or even identical to the optimal ones.

Abstract. Automatic cost analysis of programs has been traditionally studied in terms of a number of concrete, predefined resources such as execution steps, time, or memory. However, the increasing relevance of analysis applications such as static debugging and/or certification of user-level properties (including for mobile code) makes it interesting to develop analyses for resource notions that are actually applicationdependent. This may include, for example, bytes sent or received by an application, number of files left open, number of SMSs sent or received, number of accesses to a database, money spent, energy consumption, etc. We present a fully automated analysis for inferring upper bounds on the usage that a Java bytecode program makes of a set of application programmer-definable resources. In our context, a resource is defined by programmer-provided annotations which state the basic consumption that certain program elements make of that resource. From these definitions our analysis derives functions which return an upper bound on the usage that the whole program (and individual blocks) make of that resource for any given set of input data sizes. The analysis proposed is independent of the particular resource. We also present some experimental results from a prototype implementation of the approach covering an ample set of interesting resources. 1