Machine Learning Applications in Software Engineering

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. Contents:Introduction to Machine Learning and Software EngineeringML Applications in Prediction and EstimationML Applications in Property and Model DiscoveryML Applications in TransformationML Applications in Generation and SynthesisML Applications in ReuseML Applications in Requirement AcquisitionML Applications in Management of Development Knowledge Readership: College instructors, college students, researchers and software engineers. Keywords:Software Engineering;Machine Learning;Artificial Intelligence;Algorithms;Computer ProgramsKey Features:This is the first book that deals with machine learning applications in software engineering, an emerging niche area of great importanceIt offers a detailed, systematic and extensive treatment of the machine learning algorithms applicable to software engineering tasksThe book has an extensive reference list on existing work