Machine Learning Applications in Software Engineering

By: Du Zhang (Editor), Jeffrey J P Tsai (Editor)

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Hardcover | 1 February 2005

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Hardcover
368 Pages

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27.94 x 21.59 x 2.54

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Machine learning deals with the issue of how to build computer programs that improve their performance at some tasks through experience. Machine learning algorithms have proven to be of great practical value in a variety of application domains. Not surprisingly, the field of software engineering turns out to be a fertile ground where many software development and maintenance tasks could be formulated as learning problems and approached in terms of learning algorithms. This book deals with the subject of machine learning applications in software engineering. It provides an overview of machine learning, summarizes the state-of-the-practice in this niche area, gives a classification of the existing work, and offers some application guidelines. Also included in the book is a collection of previously published papers in this research area.

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Non-Fiction Computing & I.T.Computer Science Mathematical Theory of Computation Artificial Intelligence Machine Learning Computer Programming & Software Development Software Engineering Systems Analysis & Design Algorithms & Data Structures Booktopia Publisher Services World Scientific Publishing

Introduction of machine learning and software engineering	p. 1
ML applications in prediction and estimation	p. 37
Bayesian analysis of empirical software engineering cost models	p. 41
Machine learning approaches to estimating software development effort	p. 52
Estimating software project effort using analogies	p. 64
A critique of software defect prediction models	p. 72
Using regression trees to classify fault-prone software modules	p. 87
Can genetic programming improve software effort estimation? : a comparative evaluation	p. 95
Optimal software release scheduling based on artificial neural networks	p. 106
ML applications in property and model discovery	p. 125
Identifying objects in procedural programs using clustering neural networks	p. 127
Bayesian-learning based guidelines to determine equivalent mutants	p. 150
ML applications in transformation	p. 165
Using neural networks to modularize software	p. 167
ML applications in generation and synthesis	p. 199
Generating software test data by evolution	p. 201
ML applications in reuse	p. 227
On the reuse of software : a case-based approach employing a repository	p. 229
ML applications in requirement acquisition	p. 261
Inductive specification recovery : understanding software by learning from example behaviors	p. 263
Explanation-based scenario generation for reactive system models	p. 286
ML applications in management of development knowledge	p. 307
Case-based knowledge management tools for software development	p. 309
Guidelines and conclusion	p. 331
Table of Contents provided by Blackwell. All Rights Reserved.

Machine Learning Applications in Software Engineering

At a Glance

Hardcover

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