This book addresses action research (AR), one of the main research methodologies used for academia-industry research collaborations. It elaborates on how to find the right research activities and how to distinguish them from non-significant ones. Further, it details how to glean lessons from the research results, no matter whether they are positive or negative. Lastly, it shows how companies can evolve and build talents while expanding their product portfolio. The book’s structure is based on that of AR projects; it sequentially covers and discusses each phase of the project. Each chapter shares new insights into AR and provides the reader with a better understanding of how to apply it. In addition, each chapter includes a number of practical use cases or examples. Taken together, the chapters cover the entire software lifecycle: from problem diagnosis to project (or action) planning and execution, to documenting and disseminating results, including validity assessments for AR studies. The goal of this book is to help everyone interested in industry-academia collaborations to conduct joint research. It is for students of software engineering who need to learn about how to set up an evaluation, how to run a project, and how to document the results. It is for all academics who aren’t afraid to step out of their comfort zone and enter industry. It is for industrial researchers who know that they want to do more than just develop software blindly. And finally, it is for stakeholders who want to learn how to manage industrial research projects and how to set up guidelines for their own role and expectations.

"This book presents current, effective software engineering methods for the design and development of modern Web-based applications"--Provided by publisher.

This book constitutes the thoroughly refereed post-proceedings of the First International Conference on Software Engineering Research and Applications, SERA 2003, held in San Francisco, CA, USA in June 2003. The 23 revised full papers presented were carefully selected from 104 initial submissions during two rounds of reviewing and improvement. The papers are organized in topical sections on formal methods; component-based software engineering; software quality, requirements engineering, reengineering, and performance analysis; knowledge discovery and artificial intelligence; and database retrieval and human-computer interaction.

This book constitutes the thoroughly refereed post-proceedings of the Second International Conference on Software Engineering Research and Applications, SERA 2004, held in May 2004. The 18 revised full papers presented together with four keynote addresses were carefully selected from 103 initial submissions during two rounds of reviewing and improvement. The papers are organized in topical sections. These include formal methods and tools, requirements engineering and reengineering, and information engineering.

"This book provides analysis, characterization and refinement of software engineering data in terms of machine learning methods. It 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 while offering readers suggestions by proposing future work in this emerging research field"--Provided by publisher.

Writing and running software is now as much a part of science as telescopes and test tubes, but most researchers are never taught how to do either well. As a result, it takes them longer to accomplish simple tasks than it should, and it is harder for them to share their work with others than it needs to be. This book introduces the concepts, tools, and skills that researchers need to get more done in less time and with less pain. Based on the practical experiences of its authors, who collectively have spent several decades teaching software skills to scientists, it covers everything graduate-level researchers need to automate their workflows, collaborate with colleagues, ensure that their results are trustworthy, and publish what they have built so that others can build on it. The book assumes only a basic knowledge of Python as a starting point, and shows readers how it, the Unix shell, Git, Make, and related tools can give them more time to focus on the research they actually want to do. Research Software Engineering with Python can be used as the main text in a one-semester course or for self-guided study. A running example shows how to organize a small research project step by step; over a hundred exercises give readers a chance to practice these skills themselves, while a glossary defining over two hundred terms will help readers find their way through the terminology. All of the material can be re-used under a Creative Commons license, and all royalties from sales of the book will be donated to The Carpentries, an organization that teaches foundational coding and data science skills to researchers worldwide.

The development, design, and implementation of mobile communications technologies significantly impacts innovative networked applications and information services. The Handbook of Research on Mobile Software Engineering: Design Implementation and Emergent Applications offers a compendium of state-of-the-art research on the key issues surrounding current and future challenges for the software engineering of mobile systems and related emergent applications. This reference addresses gaps in the literature within the disciplines of software engineering and mobile computing.