A Revised Textual Tree Trace Notation for Prolog

This paper describes a ''textual tree trace'' (TTT) notation for representing the execution of Prolog programs. Compact, textual and non-linear, it provides detailed information about variable binding and execution history, and distinguishes several modes of goal failure. The revised form given here, yet to be empirically tested, is partly informed by Paul Mulholland's empirical comparisons of Prolog trace notations, in which an earlier version of the TTT notation was amongst those studied and criticised. The work presented here is an updated version of a previous workshop paper (Taylor, du Boulay, & Patel, 1994)

Frame-Based Editing: Easing the Transition from Blocks to Text-Based Programming

Block-based programming systems, such as Scratch or Alice, are the most popular environments for introducing young children to programming. However, mastery of text-based programming continues to be the educational goal for stu- dents who continue to program into their teenage years and beyond. Transitioning across the significant gap between the two editing styles presents a difficult challenge in school- level teaching of programming. We propose a new style of program manipulation to bridge the gap: frame-based edit- ing. Frame-based editing has the resistance to errors and approachability of block-based programming while retaining the flexibility and more conventional programming seman- tics of text-based programming languages. In this paper, we analyse the issues involved in the transition from blocks to text and argue that they can be overcome by using frame- based editing as an intermediate step. A design and imple- mentation of a frame-based editor is provided

Learning Semantic Correspondences in Technical Documentation

We consider the problem of translating high-level textual descriptions to formal representations in technical documentation as part of an effort to model the meaning of such documentation. We focus specifically on the problem of learning translational correspondences between text descriptions and grounded representations in the target documentation, such as formal representation of functions or code templates. Our approach exploits the parallel nature of such documentation, or the tight coupling between high-level text and the low-level representations we aim to learn. Data is collected by mining technical documents for such parallel text-representation pairs, which we use to train a simple semantic parsing model. We report new baseline results on sixteen novel datasets, including the standard library documentation for nine popular programming languages across seven natural languages, and a small collection of Unix utility manuals.Comment: accepted to ACL-201

A graph rewriting programming language for graph drawing

This paper describes Grrr, a prototype visual graph drawing tool. Previously there were no visual languages for programming graph drawing algorithms despite the inherently visual nature of the process. The languages which gave a diagrammatic view of graphs were not computationally complete and so could not be used to implement complex graph drawing algorithms. Hence current graph drawing tools are all text based. Recent developments in graph rewriting systems have produced computationally complete languages which give a visual view of graphs both whilst programming and during execution. Grrr, based on the Spider system, is a general purpose graph rewriting programming language which has now been extended in order to demonstrate the feasibility of visual graph drawing