Artificial Intelligence and Software Engineering

560 pages, parution le 10/12/1991

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Résumé

Chapter 1 Introduction to Computer Software: Computers and software systems; An introduction to software engineering; Bridges and buildings versus software systems; The software crisis; A demand for more software power; Responsiveness to human users; Software systems in new types of domains; Responsiveness to dynamic usage environments; Software systems with self-maintenance capabilities; A need for AI systems

Chapter 2 AI Problems and Conventional SE Problems: What is an AI problem?; Ill-defined specifications; Correct versus `good enough' solutions; It's the HOW not the WHAT; The problem of dynamics; The quality of modular approximations; Context-free problems

Chapter 3 Software Engineering Methodology: Specify and verify--the SAV methodology; The myth of complete specification; What is verifiable?; Specify and test--the SAT methodology; Testing for reliability; The strengths; The weaknesses; What are the requirements for testing?; What's in a specification'; Prototyping as a link

Chapter 4 An Incremental and Exploratory Methodology: Classical methodology and AI problems; The RUDE cycle; How do we start?; Malleable software; AI muscles on a conventional skeleton; How do we proceed'; How do we finish?; The question of hacking; Conventional paradigms

Chapter 5 New Paradigms for System Engineering: Automatic programming; Transformational implementation; The "new paradigm" of Balzer, Cheatham and Green; Operational requirements of Kowalski; The POLITE methodology

Chapter 6 Towards a Discipline of Exploratory Programming: Reverse engineering; Reusable software; Design knowledge; Stepwise abstraction; The problem of decompiling; Controlled modification; Structured growth

Chapter 7 Machine Learning: Much Promise, Many Problems: Self-adaptive software; The promise of increased software power; The threat of increased software problems; The state of the art in machine learning; Practical machine learning examples; Multiversion inductive programming

Chapter 8 Expert Systems: The Success Story: Expert Systems as AI software; Engineering expert systems; The lessons of expert systems for engineering AI software

Chapter 9 AI into Practical Software: Support environments; Reduction of effective complexity; Moderately stupid assistance; An engineering toolbox; Self-reflective software; Overengineering software

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