In this paper we will give a small introduction to Spar, a programming language for semi-automatic parallel programming. Spar, a Java derivative is being developed as part of the Automap project, which aims to create completely automatic mappings for code and data at either compile time or runtime. We will present how Spar supports task-parallel programming and how it interacts with the user or...

NUT is a programming system for knowledge-based programming with facilities for automatic program synthesis. The system allows to specify computational problems in OO style. Concepts and objects can be treated in NUT as functional constraint networks. Constraint satisfaction problems are solved using algorithms for automatic program synthesis described earlier for the PRIZ system.

Industrial plants are controlled using sequence control programs running on programmable controllers. Sequence control program design has been carried out manually, and an increase in applications of pro-grammable controllers has caused a shortage of programmers. Therefore , automatic programming systems are strongly required in this field. Controllers receive operation signals from plant opera...

Automatic programming (AP) is an important area of Machine Learning (ML) where computer programs are generated automatically. Swarm Programming (SP), a newly emerging research in AP, automatically generates the using Intelligence (SI) algorithms. This paper presents two grammar-based SP methods named as Grammatical Moth-Flame Optimizer (GMFO) and Whale (GWO). The Optimization algorithm used sea...

The future of software engineering lies in automation, and will exploit the combined strengths of generative programming, domain-specific languages, and automatic programming. While each of these areas is still in its infancy, a spectacularly successful example of their combination was realized twenty-five years ago: relational query optimization. In this paper, I chart the successes and mindse...

The future of software engineering lies in automation and will exploit the combined strengths of generative programming, domain-specific languages, and automatic programming. While each of these areas is still in its infancy, a spectacularly successful example of their combination was realized twenty-five years ago: relational query optimization. In this paper, I chart the successes and mindset...

Most work in automatic programming has focused primarily on the roles of deduction and programming knowledge However, the role played by knowledge of the task domain seems to be at least as important, both for the usability of an automatic programming system and for the feasibility of building one which works on non-trivial problems This perspective has evolved during the course of a variety of...

The goal in automatic programming is to get a computer to perform a task by telling it what needs to be done, rather than by explicitly programming it. This paper considers the task of automatically generating a computer program to enable an autonomous mobile robot to perform the task of moving a box from the middle of an irregular shaped room to the wall. We compare the ability of the recently...

Memo functions and memoization are well known concepts in AI programming. They have been around since the sixties and are often used as examples in introductory programming texts. However, the automation of memoization as a practical software engineering tool for AI systems has never received a detailed treatment. This paper describes how automatic memoization can be made viable on a large scal...