Program representation size in an intermediate language with intersection and union types

The CIL compiler for core Standard ML compiles whole programs using a novel typed intermediate language (TIL) with intersection and union types and flow labels on both terms and types. The CIL term representation duplicates portions of the program where intersection types are introduced and union types are eliminated. This duplication makes it easier to represent type information and to introduce customized data representations. However, duplication incurs compile-time space costs that are potentially much greater than are incurred in TILs employing type-level abstraction or quantification. In this paper, we present empirical data on the compile-time space costs of using CIL as an intermediate language. The data shows that these costs can be made tractable by using sufficiently fine-grained flow analyses together with standard hash-consing techniques. The data also suggests that non-duplicating formulations of intersection (and union) types would not achieve significantly better space complexity.National Science Foundation (CCR-9417382, CISE/CCR ESS 9806747); Sun grant (EDUD-7826-990410-US); Faculty Fellowship of the Carroll School of Management, Boston College; U.K. Engineering and Physical Sciences Research Council (GR/L 36963, GR/L 15685

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

Pushdown Control-Flow Analysis of Higher-Order Programs

Context-free approaches to static analysis gain precision over classical approaches by perfectly matching returns to call sites---a property that eliminates spurious interprocedural paths. Vardoulakis and Shivers's recent formulation of CFA2 showed that it is possible (if expensive) to apply context-free methods to higher-order languages and gain the same boost in precision achieved over first-order programs. To this young body of work on context-free analysis of higher-order programs, we contribute a pushdown control-flow analysis framework, which we derive as an abstract interpretation of a CESK machine with an unbounded stack. One instantiation of this framework marks the first polyvariant pushdown analysis of higher-order programs; another marks the first polynomial-time analysis. In the end, we arrive at a framework for control-flow analysis that can efficiently compute pushdown generalizations of classical control-flow analyses.Comment: The 2010 Workshop on Scheme and Functional Programmin

Database Programming in Machiavelli - A Polymorphic Language With Static Type Inference

Machiavelli is a polymorphically typed programming language in the spirit of ML, but supports a different type inferencing that makes its polymorphism somewhat more general than that of ML and appropriate for database applications. In particular, a function that selects a field f of a records is polymorphic in the sense that it can be applied to any record which contains a field f with the appropriate type. When combined with a set data type and database operations including join and projection, this provides a natural medium for relational database programming. Moreover, by implementing database objects as reference types and generating the appropriate views - sets of structures with identity - we can achieve a degree of static type checking for object-oriented databases

Polymorphism and Type Inference in Database Programming

The polymorphic type system of ML can be extended in two ways that make it appropriate as the basis of a database programming language. The first is an extension to the language of types that captures the polymorphic nature of field selection; the second is a technique that generalizes relational operators to arbitrary data structures. The combination provides a statically typed language in which relational databases may be cleanly represented as typed structures. As in ML types are inferred, which relieves the programmer of making the rather complicated type assertions that may be required to express the most general type of a program that involves field selection and generalized relational operators. It is also possible to use these ideas to implement various aspects of object-oriented databases. By implementing database objects as reference types and generating the appropriate views - sets of structures with identity - we can achieve a degree of static type checking for object-oriented databases. Moreover it is possible to exploit the type system to check the consistency of object-oriented classes (abstract data types with inheritance). A prototype language based on these ideas has been implemented. While it lacks some important practical features, it demonstrates that a wide variety of database structures can be cleanly represented in a polymorphic programming language