Zhang D., Tsai J. (eds.) Advances in Machine Learning Applications in Software Engineering

Idea Group, 2007, -384 p.Machine learning is the study of how to build computer programs that improve their performance at some task through experience. The hallmark of machine learning is that it results in an improved ability to make better decisions. 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.
To meet the challenge of developing and maintaining large 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. The past two decades have witnessed an increasing interest, and some encouraging results and publications in machine learning application to software engineering. As a result, a crosscutting niche area emerges. Currently, there are efforts to raise the awareness and profile of this crosscutting, emerging area, and to systematically study various issues in it. It is our intention to capture, in this book, some of the latest advances in this emerging niche area.Section I: Data Analysis and Refinement
A Two-Stage Zone Regression Method for Global Characterization of a Project Database
Intelligent Analysis of Software Maintenance Data
Improving Credibility of Machine Learner Models in Software Engineering
Section II: Applications to Software Development
ILP Applications to Software Engineering
MMIR: An Advanced Content-Based Image Retrieval System Using a Hierarchical Learning Framework
A Genetic Algorithm-Based QoS Analysis Tool for Reconfigurable Service-Oriented Systems
Section III: Predictive Models for Software Quality and Relevancy
Fuzzy Logic Classifiers and Models in Quantitative Software Engineering
Modeling Relevance Relations Using Machine Learning Techniques
A Practical Software Quality Classification Model Using Genetic Programming
A Statistical Framework for the Prediction of Fault-Proneness Section IV: State-of-the-Practice
Section IV:State-of-the-Practice
Applying Rule Induction in Software Prediction
Application of Genetic Algorithms in Software Testing
Formal Methods for Specifying and Analyzing Complex Software Systems
Practical Considerations in Automatic Code Generation
DPSSEE: A Distributed Proactive Semantic Software Engineering Environment
Adding Context in to An Access Control Model for Computer Security Policy