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Advances in machine learning applications in software engineering/ Du Zhang and Jeffery J.P. Tsai

By: Contributor(s): Material type: TextLanguage: English Publication details: United States: Idea Group Publishing, 2007.Description: xv, 480 p. : 24 cmISBN:
  • 9781591409410
Subject(s): DDC classification:
  • 22 005.1 ZHA
Contents:
Section I: Data Analysis and Refinement Chapter I A Two-Stage Zone Regression Method for Global Characterization of a Project Database
Chapter II Intelligent Analysis of Software Maintenance Data
Chapter III Improving Credibility of Machine Learner Models in Software Engineering
Chapter IV ILP Applications to Software Engineering
Chapter V MMIR: An Advanced Content-Based Image Retrieval System Using a Hierarchical Learning Framework
Chapter VI A Genetic Algorithm-Based QoS Analysis Tool for Reconfigurable Service-Oriented Systems
Section II: Applications to Software Development Chapter IV ILP Applications to Software Engineering.
Chapter V MMIR: An Advanced Content-Based Image Retrieval System Using a Hierarchical Learning Framework
Chapter VI A Genetic Algorithm-Based QoS Analysis Tool for Reconfigurable Service-Oriented Systems
Section III: Predictive Models for Software Quality and Relevancy Chapter VII Fuzzy Logic Classifiers and Models in Quantitative Software Engineering
Chapter VIII Modeling Relevance Relations Using Machine Learning Techniques.
Chapter IX A Practical Software Quality Classification Model Using Genetic Programming..
Chapter X A Statistical Framework for the Prediction of Fault-Proneness..
Chapter XI Applying Rule Induction in Software Prediction
Chapter XII Application of Genetic Algorithms in Software Testing
Section V: Areas of Future Work Chapter XIII Formal Methods for Specifying and Analyzing Complex Software Systems.
Chapter XIV Practical Considerations in Automatic Code Generation
Chapter XV DPSSEE: A Distributed Proactive Semantic Software Engineering Environment
Chapter XVI Adding Context into an Access Control Model for Computer Security Policy
Summary: Machine learning is the study of building computer programs that improve their performance through experience. To meet the challenge of developing and maintaining larger and complex software systems in a dynamic and changing environment, machine learning methods have been playing an increasingly important role in many software development and maintenance tasks. Advances in Machine Learning Applications in Software Engineering provides analysis, characterization, and refinement of software engineering data in terms of machine learning methods. This book depicts applications of several machine learning approaches in software systems development and deployment, and the use of machine learning methods to establish predictive models for software quality. Advances in Machine Learning Applications in Software Engineering offers readers direction for future work in this emerging research field.
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General Books CUTN Central Library Philosophy & psychology Non-fiction 005.1 ZHA (Browse shelf(Opens below)) Available 51442

Section I: Data Analysis and Refinement
Chapter I

A Two-Stage Zone Regression Method for Global Characterization of a Project
Database

Chapter II
Intelligent Analysis of Software Maintenance Data

Chapter III
Improving Credibility of Machine Learner Models in Software Engineering

Chapter IV
ILP Applications to Software Engineering

Chapter V MMIR: An Advanced Content-Based Image Retrieval System Using a
Hierarchical Learning Framework

Chapter VI
A Genetic Algorithm-Based QoS Analysis Tool for Reconfigurable
Service-Oriented Systems

Section II: Applications to Software Development
Chapter IV ILP Applications to Software Engineering.

Chapter V
MMIR: An Advanced Content-Based Image Retrieval System Using a
Hierarchical Learning Framework

Chapter VI
A Genetic Algorithm-Based QoS Analysis Tool for Reconfigurable
Service-Oriented Systems

Section III: Predictive Models for Software Quality and Relevancy
Chapter VII
Fuzzy Logic Classifiers and Models in Quantitative Software Engineering

Chapter VIII
Modeling Relevance Relations Using Machine Learning Techniques.

Chapter IX
A Practical Software Quality Classification Model Using Genetic
Programming..

Chapter X
A Statistical Framework for the Prediction of Fault-Proneness..

Chapter XI
Applying Rule Induction in Software Prediction

Chapter XII
Application of Genetic Algorithms in Software Testing

Section V: Areas of Future Work
Chapter XIII
Formal Methods for Specifying and Analyzing Complex Software Systems.

Chapter XIV
Practical Considerations in Automatic Code Generation

Chapter XV
DPSSEE: A Distributed Proactive Semantic Software Engineering
Environment

Chapter XVI
Adding Context into an Access Control Model for Computer Security Policy

Machine learning is the study of building computer programs that improve their performance through experience. To meet the challenge of developing and maintaining larger and complex software systems in a dynamic and changing environment, machine learning methods have been playing an increasingly important role in many software development and maintenance tasks.

Advances in Machine Learning Applications in Software Engineering provides analysis, characterization, and refinement of software engineering data in terms of machine learning methods. This book depicts applications of several machine learning approaches in software systems development and deployment, and the use of machine learning methods to establish predictive models for software quality. Advances in Machine Learning Applications in Software Engineering offers readers direction for future work in this emerging research field.

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