We provide a method whereby, given mode and (upper approximation) type information, we can detect procedures and goals that can be guaranteed to not fail (i.e., to produce at least one solution or not terminate). The technique is based on an intuitively very simple notion, that of a (set of) tests "covering" the type of a set of variables. We show that the problem of determining a covering is undecidable in general, and give decidability and complexity results for the Herbrand and linear arithmetic constraint systems. We give sound algorithms for determining covering that are precise and efficient in practice. Based on this information, we show how to identify goals and procedures that can be guaranteed to not fail at runtime. Applications of such non-failure information include programming error detection, program transformations and parallel execution optimization, avoiding speculative parallelism and estimating lower bounds on the computational costs of goals, which can be used for ...

This paper presents a new notion of typing for logic programs which generalizes the notion of directional types. The generation of type dependencies for a logic program is fully automatic with respect to a given domain of types. The analysis method is based on a novel combination of program abstraction and ACI-unification which is shown to be correct and optimal. Type dependencies are obtained by abstracting programs, replacing concrete terms by their types, and evaluating the meaning of the abstract programs using a standard semantics for logic programs enhanced by ACI-unification. This approach is generic and can be used with any standard semantics. The method is both theoretically clean and easy to implement using general purpose tools. The proposed domain of types is condensing which means that analyses can be carried out in both top-down or bottom-up frameworks with no loss of precision for goal-independent analyses. The proposed method has been fully implemented within a bottom-up approach and the experimental results are promising.

work in the ESPRIT project DiSCiPl. The project aims at devising advanced tools for debugging of constraint logic programs. Acentral problem in program development is obtaining a program which satis es the user's expectations. When considering a given program, a natural question is then whether or not it

We present a tutorial overview of Ciaopp, the Ciao system preprocessor. Ciao is a public-domain, next-generation logic programming system, which subsumes ISO-Prolog and is specifically designed to a) be highly extensible via libraries and b) support modular program analysis, debugging, and optimization. The latter tasks are performed in an integrated fashion by Ciaopp. Ciaopp uses modular, incremental abstract interpretation to infer properties of program predicates and literals, including types, variable instantiation properties (including modes), non-failure, determinacy, bounds on computational cost, bounds on sizes of terms in the program, etc. Using such analysis information, Ciaopp can find errors at compile-time in programs and/or perform partial verification. Ciaopp checks how programs call system libraries and also any assertions present in the program or in other modules used by the program. These assertions are also used to generate documentation automatically. Ciaopp also uses...

This paper contains two main parts. The first examines the set constraint calculus, discusses its history, and overviews the current state of known algorithms and related issues. Here we will also survey the uses of set constraints, starting from early work in (imperative) program analysis, to more recent work in logic and functional programming systems. The second part describes set-based analysis. The aim here is a declarative interpretation of what it means to approximate the meaning of a program in just one way: ignore dependencies between variables, and instead, reason about each variable as the set of its possible runtime values. The basic approach starts with some description of the operational semantics, and then systematically replaces descriptions of environments (mappings from program variables to values) by set environments (mappings from program variables to sets

Standard partial deduction suffers from several drawbacks when compared to topdown abstract interpretation schemes. Conjunctive partial deduction, an extension of standard partial deduction, remedies one of those, namely the lack of side-ways information passing. But two other problems remain: the lack of success-propagation as well as the lack of inference of global success-information. We illustrate these drawbacks and show how they can be remedied by combining conjunctive partial deduction with an abstract interpretation technique known as more specific program construction. We present a simple, as well as a more refined integration of these methods. Finally we illustrate the practical relevance of this approach for some advanced applications, like proving functionality or specialising certain meta-programs written in the ground representation, where it surpasses the precision of current abstract interpretation techniques. 1 Introduction The heart of any technique for partial deduc...

Abstract. The technique of Abstract Interpretation has allowed the development of very sophisticated global program analyses which are at the same time provably correct and practical. We present in a tutorial fashion a novel program development framework which uses abstract interpretation as a fundamental tool. The framework uses modular, incremental abstract interpretation to obtain information about the program. This information is used to validate programs, to detect bugs with respect to partial specifications written using assertions (in the program itself and/or in system libraries), to generate and simplify run-time tests, and specialization, parallelization, and resource usage control, all in a provably correct way. In the case of validation and debugging, the assertions can refer to a variety of program points such as procedure entry, procedure exit, points within procedures, or global computations. The system can reason with much richer information than, for example, traditional types. This includes data structure shape (including pointer sharing), bounds on data structure sizes, and other operational variable instantiation properties, as well as procedure-level properties such as determinacy, termination, non-failure, and bounds on resource consumption (time or space cost). CiaoPP, the preprocessor of the Ciao multi-paradigm programming system, which implements the described functionality, will be used to illustrate the fundamental ideas.

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The declarative semantics of definite logic programs is the basis of an elegant and practical framework for their static analysis. We define a core semantics parameterised by a preinterpretation of the language underlying the program. The concrete semantics is given by an extended Herbrand interpretation, capturing the correct answers of a program. The semantics is computed as the least fixed point of an immediate consequences operator. An abstract semantics is specified simply by giving, for each constant and function in the program, a denotation in an abstract domain of interpretation. No abstract operations such as abstract unification need to be defined. The directness and simplicity of this approach is then illustrated by specifying and implementing a number of abstract interpretations. These include various mode analyses, analyses on the structure of lists and the length of lists, and simple and polymorphic types. The implementations used for the experiments are based on abstract...

Abstract. Set-based program analysis has many potential applications, including compiler optimisations, type-checking, debugging, verification and planning. One method of set-based analysis is to solve a set of set constraints derived directly from the program text. Another approach is based on abstract interpretation (with widening) over an infinite-height domain of regular types. Up till now only deterministic types have been used in abstract interpretations, whereas solving set constraints yields non-deterministic types, which are more precise. It was pointed out by Cousot and Cousot that set constraint analysis of a particular program P could be understood as an abstract interpretation over a finite domain of regular tree grammars, constructed from P. In this paper we define such an abstract interpretation for logic programs, formulated over a domain of non-deterministic finite tree automata, and describe its implementation. Both goal-dependent and goal-independent analysis are considered. Variations on the abstract domains operations are introduced, and we discuss the associated tradeoffs of precision and complexity. The experimental results indicate that this approach is a practical way of achieving the precision of set-constraints in the abstract interpretation framework. 1