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Guide to Advanced Empirical Software Engineering
This book gathers chapters from some of the top international empirical software engineering researchers focusing on the practical knowledge necessary for conducting, reporting and using empirical methods in software engineering. Topics and features include guidance on how to design, conduct and report empirical studies. The volume also provides information across a range of techniques, methods and qualitative and quantitative issues to help build a toolkit applicable to the diverse software development contexts
Empirical Software Engineering Issues. Critical Assessment and Future Directions
This book constitutes the thoroughly refereed post-proceedings of the International Dagstuhl-Seminar on Empirical Software Engineering, held in Dagstuhl Castle, Germany in June 2006. The 54 revised full papers in this state-of-the-art survey are organized in topical sections on the empirical paradigm, measurement and model building, technology transfer and education, as well as roadmapping.
Agile Methods
This book constitutes revised selected papers from the 9th Brazilian Workshop on Agile Methods, WBMA 2018, held in Campinas, Brazil, in October 2018. The 6 full and 1 short papers presented in this volume were carefully reviewed and selected from 18 submissions. Accepted papers in this edition present empirical results and literature reviews on agile requirements validation in Brazilian software development companies; a survey on Brazilian software processes about to be agile or not; an evaluation of an agile maturity model; strategies to increase customer value in agile software development; an agile development environment and scrum in a strongly hierarchical organization.
A Knowledge-Based Approach to Program Understanding
Program understanding plays an important role in nearly all software related tasks. It is vital to the development, maintenance and reuse activities. Program understanding is indispensable for improving the quality of software development. Several development activities such as code reviews, debugging and some testing approaches require programmers to read and understand programs. Maintenance activities cannot be performed without a deep and correct understanding of the component to be maintained. Program understanding is vital to the reuse of code components because they cannot be utilized without a clear understanding of what they do. If a candidate reusable component needs to be modified,...
Perspectives on the Future of Software Engineering
The dependence on quality software in all areas of life is what makes software engineering a key discipline for today’s society. Thus, over the last few decades it has been increasingly recognized that it is particularly important to demonstrate the value of software engineering methods in real-world environments, a task which is the focus of empirical software engineering. One of the leading protagonists of this discipline worldwide is Prof. Dr. Dr. h.c. Dieter Rombach, who dedicated his entire career to empirical software engineering. For his many important contributions to the field he has received numerous awards and recognitions, including the U.S. National Science Foundation’s Pres...
Foundations of Empirical Software Engineering
Although software engineering can trace its beginnings to a NATO conf- ence in 1968, it cannot be said to have become an empirical science until the 1970s with the advent of the work of Prof. Victor Robert Basili of the University of Maryland. In addition to the need to engineer software was the need to understand software. Much like other sciences, such as physics, chemistry, and biology, software engineering needed a discipline of obs- vation, theory formation, experimentation, and feedback. By applying the scientific method to the software engineering domain, Basili developed concepts like the Goal-Question-Metric method, the Quality-Improvement- Paradigm, and the Experience Factory to he...
Young Children
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Data Analytics for Cybersecurity
Shows how traditional and nontraditional methods such as anomaly detection and time series can be extended using data analytics.
