Advances in Machine Learning Applications in Software Engineering, 1st Edition

  • Published By:
  • ISBN-10: 1591409438
  • ISBN-13: 9781591409434
  • Grade Level Range: College Freshman - College Senior
  • 300 Pages | eBook
  • Original Copyright 2006 | Published/Released April 2007
  • This publication's content originally published in print form: 2006

  • Price:  Sign in for price

About

Overview

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.

Table of Contents

Front Cover.
Title Page.
Copyright Page.
Advances in Machine Learning Applications in Software Engineering: Table of Contents.
Preface.
Acknowledgments.
1: Data Analysis and Refinement.
2: A Two-Stage Zone Regression Method for Global Characterization of a Project Database.
3: Intelligent Analysis of Software Maintenance Data.
4: Improving Credibility of Machine Learner Models in Software Engineering.
5: Applications to Software Development.
6: ILP Applications to Software Engineering.
7: MMIR: An Advanced Content-Based Image Retrieval System Using a Hierarchical Learning Framework.
8: A Genetic Algorithm-Based QoS Analysis Tool for Reconfigurable Service-Oriented Systems.
9: Predictive Models for Software Quality and Relevancy.
10: Fuzzy Logic Classifiers and Models in Quantitative Software Engineering.
11: Modeling Relevance Relations Using Machine Learning Techniques.
12: A Practical Software Quality Classification Model Using Genetic Programming.
13: A Statistical Framework for the Prediction of Fault-Proneness.
14: State-of-the-Practice.
15: Applying Rule Induction in Software Prediction.
16: Application of Genetic Algorithms in Software Testing.
17: Areas of Future Work.
18: Formal Methods for Specifying and Analyzing Complex Software Systems.
19: Practical Considerations in Automatic Code Generation.
20: DPSSEE: A Distributed Proactive Semantic Software Engineering Environment.
21: Adding Context into an Access Control Model for Computer Security Policy.
About the Editors.
About the Authors.
Index.