Foundations of Programming Languages

This clearly written textbook provides an accessible introduction to the three programming paradigms of object-oriented/imperative, functional, and logic programming. Highly interactive in style, the text encourages learning through practice, offering test exercises for each topic covered. Review questions and programming projects are also presented, to help reinforce the concepts outside of the classroom. This updated and revised new edition features new material on the Java implementation of the JCoCo virtual machine

Approaches to Interpreter Composition

In this paper, we compose six different Python and Prolog VMs into 4 pairwise compositions: one using C interpreters; one running on the JVM; one using meta-tracing interpreters; and one using a C interpreter and a meta-tracing interpreter. We show that programs that cross the language barrier frequently execute faster in a meta-tracing composition, and that meta-tracing imposes a significantly lower overhead on composed programs relative to mono-language programs.Comment: 33 pages, 1 figure, 9 table

An Integrated Development Environment for Declarative Multi-Paradigm Programming

In this paper we present CIDER (Curry Integrated Development EnviRonment), an analysis and programming environment for the declarative multi-paradigm language Curry. CIDER is a graphical environment to support the development of Curry programs by providing integrated tools for the analysis and visualization of programs. CIDER is completely implemented in Curry using libraries for GUI programming (based on Tcl/Tk) and meta-programming. An important aspect of our environment is the possible adaptation of the development environment to other declarative source languages (e.g., Prolog or Haskell) and the extensibility w.r.t. new analysis methods. To support the latter feature, the lazy evaluation strategy of the underlying implementation language Curry becomes quite useful.Comment: In A. Kusalik (ed), proceedings of the Eleventh International Workshop on Logic Programming Environments (WLPE'01), December 1, 2001, Paphos, Cyprus. cs.PL/011104

Towards an Automated Test Bench Environment for Prolog Systems

Software testing and benchmarking is a key component of the software development process. Nowadays, a good practice in big software projects is the Continuous Integration (CI) software development technique. The key idea of CI is to let developers integrate their work as they produce it, instead of doing the integration at the end of each software module. In this paper, we extend a previous work on a benchmark suite for the Yap Prolog system and we propose a fully automated test bench environment for Prolog systems, named Yet Another Prolog Test Bench Environment (YAPTBE), aimed to assist developers in the development and CI of Prolog systems. YAPTBE is based on a cloud computing architecture and relies on the Jenkins framework and in a set of new Jenkins plugins to manage the underneath infrastructure. We present the key design and implementation aspects of YAPTBE and show its most important features, such as its graphical user interface and the automated process that builds and runs Prolog systems and benchmarks

Probabilistic Programming Concepts

A multitude of different probabilistic programming languages exists today, all extending a traditional programming language with primitives to support modeling of complex, structured probability distributions. Each of these languages employs its own probabilistic primitives, and comes with a particular syntax, semantics and inference procedure. This makes it hard to understand the underlying programming concepts and appreciate the differences between the different languages. To obtain a better understanding of probabilistic programming, we identify a number of core programming concepts underlying the primitives used by various probabilistic languages, discuss the execution mechanisms that they require and use these to position state-of-the-art probabilistic languages and their implementation. While doing so, we focus on probabilistic extensions of logic programming languages such as Prolog, which have been developed since more than 20 years

Improving the compilation of prolog to C using moded types and determinism information

We describe the current status of and provide performance results for a prototype compiler of Prolog to C, ciaocc. ciaocc is novel in that it is designed to accept different kinds of high-level information, typically obtained via an automatic analysis of the initial Prolog program and expressed in a standardized language of assertions. This information is used to optimize the resulting C code, which is then processed by an off-the-shelf C compiler. The basic translation process essentially mimics the unfolding of a bytecode emulator with respect to the particular bytecode corresponding to the Prolog program. This is facilitated by a flexible design of the instructions and their lower-level components. This approach allows reusing a sizable amount of the machinery of the bytecode emulator: predicates already written in C, data definitions, memory management routines and áreas, etc., as well as mixing emulated bytecode with native code in a relatively straightforward way. We report on the performance of programs compiled by the current versión of the system, both with and without analysis information

Improving the compilation of prolog to C using type and determinism information: Preliminary results

We describe the current status of and provide preliminary performance results for a compiler of Prolog to C. The compiler is novel in that it is designed to accept different kinds of high-level information (typically obtained via an analysis of the initial Prolog program and expressed in a standardized language of assertions) and use this information to optimize the resulting C code, which is then further processed by an off-the-shelf C compiler. The basic translation process used essentially mimics an unfolding of a C-coded bytecode emúlator with respect to the particular bytecode corresponding to the Prolog program. Optimizations are then applied to this unfolded program. This is facilitated by a more flexible design of the bytecode instructions and their lower-level components. This approach allows reusing a sizable amount of the machinery of the bytecode emulator: ancillary pieces of C code, data definitions, memory management routines and áreas, etc., as well as mixing bytecode emulated code with natively compiled code in a relatively straightforward way We report on the performance of programs compiled by the current versión of the system, both with and without analysis information