Towards an Intelligent Debugging System for Pascal Programs: On the Theory and Algorithms of Plan Recognition in Rich's Plan Calculus

This thesis presents work in progress on building an intelligent general purpose, domain-free debugging system for Pascal programs. This system (IDS) is based on Rich’s surface plan/plan calculus graph formalisms, and this thesis develops theory and algorithms for performing program understanding within this framework. This thesis is in three parts, the first part describes why an ability to do both plan recognition and general purpose reasoning is essential for debugging programs. The second part of this thesis describes the plan calculus and shows how it offers the ability to combine both general purpose reasoning, and (graph-based) plan recognition. It then goes on to present a new polynomial time algorithm (a generalisation of traditional chart parsing for string grammars) for doing bottom-up, or top-down, analysis of surface plans. This algorithm is presented in the framework of restricted structure sharing flow graph grammars developed for this purpose. To justify the use of such grammars this part of the thesis develops the theory of deterministic operations in the plan calculus, and also develops the theory of generalised control flow environments to justify the way control flow information is handled. The final part gives an overview of IDS, and modifies the above algorithm to cope with various parts of the plan calculus that do not exactly fit into the framework we have developed. Thus this thesis gives a complete account of how to perform plan recognition in Rich’s framework. This part of the thesis also describes the translation process from Pascal to surface plans, and presents a technique for translating plans expressed in Rich’s notation, into suitable rules for the parser to use. Finally it presents some preliminary work on exactly how a debugging system using these techniques might go about locating and fixing bugs in programs

Towards a debugging tutor for object-oriented environments

Programming has provided a rich domain for Artificial Intelligence in Education and many systems have been developed to advise students about the bugs in their programs, either during program development or post-hoc. Surprisingly few systems have been developed specifically to teach debugging. Learning environment builders have assumed that either the student will be taught these elsewhere or thatthey will be learnt piecemeal without explicit advice.This paper reports on two experiments on Java debugging strategy by novice programmers and discusses their implications for the design of a debugging tutor for Java that pays particular attention to how students use the variety of program representations available. The experimental results are in agreement with research in the area that suggests that good debugging performance is associated with a balanced use ofthe available representations and a sophisticated use of the debugging step facility which enables programmers to detect and obtain information from critical momentsin the execution of the program. A balanced use of the available representations seemsto be fostered by providing representations with a higher degree of dynamic linkingas well as by explicit instruction about the representation formalism employed in the program visualisations

A survey of representations employed in object-orientated programming

This document presents an overview of the program visualisations additional to the program code provided by some of the most popular object-oriented programming environments to support tasks involving program comprehension. These representations were compared in terms of the programming aspects they highlight and of their information modality. Those with common characteristics according to these criteria were identified. Finally, a brief analysis of these common representations in terms of Green's Cognitive Dimensions is presented. Two questions arising from this survey are (a) whether representations additional to the code should be redundant and highlight similar information to the main notation or be complementary and highlight different programming aspects and (b) which factors might increase the cognitive difficulty of co-ordinating these additional representations and the program code. More theoretical knowledge about the way these additional representations influence the comprehension of computer programs seems to be needed

A methodology for the capture and analysis of hybrid data: a case study of program debugging

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Protocolos de encaminhamento para oftalmologia adulto

Telemedicin

Protocolos de encaminhamento para oftalmologia pediátrica

Telemedicin

Evolving good hierarchical decompositions of complex systems

Many application areas represent the architecture of complex systems by means of hierarchical graphs containing basic entities with directed links between them, and showing the decomposition of systems into a hierarchical nested “module” structure. An interesting question is then: How best should such a complex system be decomposed into a hierarchical tree of nested “modules”? This paper describes an interesting complexity measure (based on an information theoretic minimum description length principle) which can be used to compare two such hierarchical decompositions. This is then used as the fitness function for a genetic algorithm (GA) which successfully explores the space of possible hierarchical decompositions of a system. The paper also describes the novel crosssover and mutation operators that are necessary in order to do this, and gives some examples of the system in practice

'Being clever makes you sexy! a review of The Mating Mind' [Review] Geoffrey Miller (2000) The mating mind: how sexual choice shaped the evolution of human nature

A Review of “The Mating Mind” by Geoffrey Miller, William Heinemann, London ISBN 043400741

Learning Stochastic Context-Free Grammars from Corpora Using a Genetic Algorithm.

A genetic algorithm for learning stochastic context-free grammars from finite language samples as described. Solutions to the inference problem are evolved by optimising the parameters of a covering grammar for a given language sample. We describe a number of experiments in learning grammars for a range of formal languages. The results of these experiments are encouraging and compare favourably with other approaches to stochastic grammatical inference