Machine Learning Applications In Software Engineering

This book presents fundamental topics and algorithms that form the core of machine learning (ML) research, as well as emerging paradigms in intelligent system design. The multidisciplinary nature of

Phase transitions typically occur in combinatorial computational problems and have important consequences, especially with the current spread of statistical relational learning as well as sequence learning methodologies. In Phase Transitions in Machine Learning the authors begin by describing in detail this phenomenon, and the extensive experimental investigation that supports its presence. They then turn their attention to the possible implications and explore appropriate methods for tackling them. Weaving together fundamental aspects of computer science, statistical physics and machine learning, the book provides sufficient mathematics and physics background to make the subject intelligible to researchers in AI and other computer science communities. Open research issues are also discussed, suggesting promising directions for future research.

Dataset shift is a common problem in predictive modeling that occurs when the joint distribution of inputs and outputs differs between training and test stages. Covariate shift, a particular case of

This book assesses the achievements of the software engineering discipline as represented by IT vendors in Japan in order to deepen understanding of the mechanisms of how software engineering capabili

Innovations in Computing Sciences and Software Engineering includes a set of rigorously reviewed world-class manuscripts addressing and detailing state-of-the-art research projects in the areas of Com

A practical text suitable for an introductory or advanced course in formal methods, this book presents a mathematical approach to modelling and designing systems using an extension of the B formal method: Event-B. Based on the idea of refinement, the author's systematic approach allows the user to construct models gradually and to facilitate a systematic reasoning method by means of proofs. Readers will learn how to build models of programs and, more generally, discrete systems, but this is all done with practice in mind. The numerous examples provided arise from various sources of computer system developments, including sequential programs, concurrent programs and electronic circuits. The book also contains a large number of exercises and projects ranging in difficulty. Each of the examples included in the book has been proved using the Rodin Platform tool set, which is available free for download at www.event-b.org.

-- Seven new chapters and exhaustive coverage of process improvement, testing, and quality assurance.-- Contains numerous real-life examples based on the author's work at the Malcolm Baldrige Award-wi

This textbook is intended for introductory software engineering coursework or as a reference for practicing programmers. The authors introduce types and classes of software, software development life

Machine Learning and Knowledge Discovery for Engineering Systems Health Management presents state-of-the-art tools and techniques for automatically detecting, diagnosing, and predicting the effects of

Using a problem-based quantitative approach, Striebig, Ogundipe, and Papadakis present a textbook on sustainable design and development that can be used in any field of engineering. The prerequisites

The final installment in this three-volume set is based on this maxim: "Before software can be designed its requirements must be well understood, and before the requirements can be expressed properly

This book presents the current trends, technologies, and challenges in Big Data in the diversified field of engineering and sciences. It covers the applications of Big Data ranging from conventional f