A Practical Type Analysis for Verification of Modular Prolog Programs

Regular types are a powerful tool for computing very precise descriptive types for logic programs. However, in the context of real life, modular Prolog programs, the accurate results obtained by regular types often come at the price of efficiency. In this paper we propose a combination of techniques aimed at improving analysis efficiency in this context. As a first technique we allow optionally reducing the accuracy of inferred types by using only the types defined by the user or present in the libraries. We claim that, for the purpose of verifying type signatures given in the form of assertions the precision obtained using this approach is sufficient, and show that analysis times can be reduced significantly. Our second technique is aimed at dealing with situations where we would like to limit the amount of reanalysis performed, especially for library modules. Borrowing some ideas from polymorphic type systems, we show how to solve the problem by admitting parameters in type specifications. This allows us to compose new call patterns with some pre computed analysis info without losing any information. We argue that together these two techniques contribute to the practical and scalable analysis and verification of types in Prolog programs

A Web-based Tool Combining Different Type Analyses

Abstract. There are various kinds of type analysis of logic programs. These in-clude for example inference of types that describe an over-approximation of the success set of a program, inference of well-typings, and abstractions based on given types. Analyses can be descriptive or prescriptive or a mixture of both, and they can be goal-dependent or goal-independent. We describe a prototype tool that can be accessed from a web browser, allowing various type analyses to be run. The first goal of the tool is to allow the analysis results to be examined conveniently by clicking on points in the original program clauses, and to highlight ill-typed pro-gram constructs, empty types or other type anomalies. Secondly the tool allows combination of the various styles of analysis. For example, a descriptive regular type can be automatically inferred for a given program, and then that type can be used to generate the minimal “domain model ” of the program with respect to the corresponding pre-interpretation, which can give more precise information than the original descriptive type.

Abstract verification and debugging of constraint logic programs

The technique of Abstract Interpretation [13] has allowed the development of sophisticated program analyses which are provably correct and practical. The semantic approximations produced by such analyses have been traditionally applied to optimization during program compilation. However, recently, novel and promising applications of semantic approximations have been proposed in the more general context of program verification and debugging [3],[10],[7]

Using global analysis, partial specifications, and an extensible assertion language for program validation and debugging

We discuss a framework for the application of abstract interpretation as an aid during program development, rather than in the more traditional application of program optimization. Program validation and detection of errors is first performed statically by comparing (partial) specifications written in terms of assertions against information obtained from (global) static analysis of the program. The results of this process are expressed in the user assertion language. Assertions (or parts of assertions) which cannot be checked statically are translated into run-time tests. The framework allows the use of assertions to be optional. It also allows using very general properties in assertions, beyond the predefined set understandable by the static analyzer and including properties defined by user programs. We also report briefly on an implementation of the framework. The resulting tool generates and checks assertions for Prolog, CLP(R), and CHIP/CLP(fd) programs, and integrates compile-time and run-time checking in a uniform way. The tool allows using properties such as types, modes, non-failure, determinacy, and computational cost, and can treat modules separately, performing incremental analysis

Providing Preventive Oral Health Care to Infants and Young Children in Women, Infants, and Children (WIC), Early Head Start, and Primary Care Settings

This report focuses on seven oral health programs that provide preventive oral health care to young children (infants, toddlers, and children up to 5 years old) in Special Supplemental Nutrition Program for Women, Infants, and Children (WIC), Early Head Start (EHS), and primary care settings. All of the programs strive to increase access to preventive oral health care by integrating dental services into primary care settings, WIC clinics, or EHS centers. These programs also rely on primary care providers (physicians, nurses, medical assistants, etc.) or new types of dental hygienists who can practice in community settings to deliver preventive oral health services. Two additional reports in this series describe the remaining programs that provide care in non-dental settings and programs designed to specifically address socioeconomic, cultural, and geographic barriers to preventive oral health care.The findings from the EAs of these programs are synthesized to highlight diverse and innovative strategies that are utilized to provide preventive oral health care in primary care settings, WIC clinics, or EHS centers. These strategies have potential for rigorous evaluation and could emerge as best practices. If proven effective, these innovative program elements could then be disseminated and replicated to increase access for populations in need of preventive oral health care

Supervising Offline Partial Evaluation of Logic Programs using Online Techniques

A major impediment for more widespread use of offline partial evaluation is the difficulty of obtaining and maintaining annotations for larger, realistic programs. Existing automatic binding-time analyses still only have limited applicability and annotations often have to be created or improved and maintained by hand, leading to errors. We present a technique to help overcome this problem by using online control techniques which supervise the specialisation process in order to help the development and maintenance of correct annotations by identifying errors. We discuss an implementation in the Logen system and show on a series of examples that this approach is effective: very few false alarms were raised while infinite loops were detected quickly. We also present the integration of this technique into a web interface, which highlights problematic annotations directly in the source code. A method to automatically fix incorrect annotations is presented, allowing the approach to be also used as a pragmatic binding time analysis. Finally we show how our method can be used for efficiently locating built-in errors in Prolog source code