Transformation of propositional calculus statements into integer and mixed integer programs: An approach towards automatic reformulation

A systematic procedure for transforming a set of logical statements or logical conditions imposed on a model into an Integer Linear Progamming (ILP) formulation Mixed Integer Programming (MIP) formulation is presented. An ILP stated as a system of linear constraints involving integer variables and an objective function, provides a powerful representation of decision problems through a tightly interrelated closed system of choices. It supports direct representation of logical (Boolean or prepositional calculus) expressions. Binary variables (hereafter called logical variables) are first introduced and methods of logically connecting these to other variables are then presented. Simple constraints can be combined to construct logical relationships and the methods of formulating these are discussed. A reformulation procedure which uses the extended reverse polish representation of a compound logical form is then described. These reformulation procedures are illustrated by two examples. A scheme of implementation.ithin an LP modelling system is outlined

Description and Optimization of Abstract Machines in a Dialect of Prolog

In order to achieve competitive performance, abstract machines for Prolog and related languages end up being large and intricate, and incorporate sophisticated optimizations, both at the design and at the implementation levels. At the same time, efficiency considerations make it necessary to use low-level languages in their implementation. This makes them laborious to code, optimize, and, especially, maintain and extend. Writing the abstract machine (and ancillary code) in a higher-level language can help tame this inherent complexity. We show how the semantics of most basic components of an efficient virtual machine for Prolog can be described using (a variant of) Prolog. These descriptions are then compiled to C and assembled to build a complete bytecode emulator. Thanks to the high level of the language used and its closeness to Prolog, the abstract machine description can be manipulated using standard Prolog compilation and optimization techniques with relative ease. We also show how, by applying program transformations selectively, we obtain abstract machine implementations whose performance can match and even exceed that of state-of-the-art, highly-tuned, hand-crafted emulators.Comment: 56 pages, 46 figures, 5 tables, To appear in Theory and Practice of Logic Programming (TPLP

Optimizing Abstract Abstract Machines

The technique of abstracting abstract machines (AAM) provides a systematic approach for deriving computable approximations of evaluators that are easily proved sound. This article contributes a complementary step-by-step process for subsequently going from a naive analyzer derived under the AAM approach, to an efficient and correct implementation. The end result of the process is a two to three order-of-magnitude improvement over the systematically derived analyzer, making it competitive with hand-optimized implementations that compute fundamentally less precise results.Comment: Proceedings of the International Conference on Functional Programming 2013 (ICFP 2013). Boston, Massachusetts. September, 201

Silicon compilation

Silicon compilation is a term used for many different purposes. In this paper we define silicon compilation as a mapping from some higher level description into layout. We define the basic issues in structural and behavioral silicon compilation and some possible solutions to those issues. Finally, we define the concept of an intelligent silicon compiler in which the compiler evaluates the quality of the generated design and attempts to improve it if it is not satisfactory

Test Case Generation for Object-Oriented Imperative Languages in CLP

Testing is a vital part of the software development process. Test Case Generation (TCG) is the process of automatically generating a collection of test cases which are applied to a system under test. White-box TCG is usually performed by means of symbolic execution, i.e., instead of executing the program on normal values (e.g., numbers), the program is executed on symbolic values representing arbitrary values. When dealing with an object-oriented (OO) imperative language, symbolic execution becomes challenging as, among other things, it must be able to backtrack, complex heap-allocated data structures should be created during the TCG process and features like inheritance, virtual invocations and exceptions have to be taken into account. Due to its inherent symbolic execution mechanism, we pursue in this paper that Constraint Logic Programming (CLP) has a promising unexploited application field in TCG. We will support our claim by developing a fully CLP-based framework to TCG of an OO imperative language, and by assessing it on a corresponding implementation on a set of challenging Java programs. A unique characteristic of our approach is that it handles all language features using only CLP and without the need of developing specific constraint operators (e.g., to model the heap)