This book presents joint works of members of the software engineering and formal methods communities with representatives from industry, with the goal of establishing the foundations for a common understanding of the needs for more flexibility in model-driven engineering. It is based on the Dagstuhl Seminar 19481 „Composing Model-Based Analysis Tools“, which was held November 24 to 29, 2019, at Schloss Dagstuhl, Germany, where current challenges, their background and concepts to address them were discussed. The book is structured in two parts, and organized around five fundamental core aspects of the subject: (1) the composition of languages, models and analyses; (2) the integration and orchestration of analysis tools; (3) the continual analysis of models; (4) the exploitation of results; and (5) the way to handle uncertainty in model-based developments. After a chapter on foundations and common terminology and a chapter on challenges in the field, one chapter is devoted to each of the above five core aspects in the first part of the book. These core chapters are accompanied by additional case studies in the second part of the book, in which specific tools and experiences are presented in more detail to illustrate the concepts and ideas previously introduced. The book mainly targets researchers in the fields of software engineering and formal methods as well as software engineers from industry with basic familiarity with quality properties, model-driven engineering and analysis tools. From reading the book, researchers will receive an overview of the state-of-the-art and current challenges, research directions, and recent concepts, while practitioners will be interested to learn about concrete tools and practical applications in the context of case studies.

This book constitutes the thoroughly revised selected papers from the 18th International Symposium, FACS 2022, which was held online in November 2022.The 12 full papers and 1 short paper were carefully reviewed and selected from 25 submissions. FACS 2021 is focusing on the areas of component software and formal methods in order to promote a deeper understanding of how formal methods can or should be used to make component-based software development succeed.

This book constitutes the refereed proceedings of the 4th International Conference on Tools and Methods for Program Analysis, TMPA 2017, Moscow, Russia, March 3-4, 2017. The 12 revised full papers and 5 revised short papers presented together with three abstracts of keynote talks were carefully reviewed and selected from 51 submissions. The papers deal with topics such as software test automation, static program analysis, verification, dynamic methods of program analysis, testing and analysis of parallel and distributed systems, testing and analysis of high-load and high-availability systems, analysis and verification of hardware and software systems, methods of building quality software, tools for software analysis, testing and verification.

Poor performance is one of the main quality-related shortcomings that cause software projects to fail. Thus, the need to address performance concerns early during the software development process is fully acknowledged, and there is a growing interest in the research and software industry communities towards techniques, methods and tools that permit to manage system performance concerns as an integral part of software engineering. Model-based software performance analysis introduces performance concerns in the scope of software modeling, thus allowing the developer to carry on performance analysis throughout the software lifecycle. With this book, Cortellessa, Di Marco and Inverardi provide the cross-knowledge that allows developers to tackle software performance issues from the very early phases of software development. They explain the basic concepts of performance analysis and describe the most representative methodologies used to annotate and transform software models into performance models. To this end, they go all the way from performance primers through software and performance modeling notations to the latest transformation-based methodologies. As a result, their book is a self-contained reference text on software performance engineering, from which different target groups will benefit: professional software engineers and graduate students in software engineering will learn both basic concepts of performance modeling and new methodologies; while performance specialists will find out how to investigate software performance model building.

In this work, the authors analysed the co-dependency between models and analyses, particularly the structure and interdependence of artefacts and the feature-based decomposition and composition of model-based analyses. Their goal is to improve the maintainability of model-based analyses. They have investigated the co-dependency of Domain-specific Modelling Languages (DSMLs) and model-based analyses regarding evolvability, understandability, and reusability.

Technological change is one of the greatest issues in the modern world. As the world faces societal challenges, e.g., climate challenges, aging problem, and energy security, technology will contribute to new or better solutions for those problems. New technologies take time to develop and mature; moreover, they tend to be born in the gaps of multiple technology fields; therefore, early detection of emerging technological concepts across multiple disciplines will be a very important issue. Our goal seeks to develop automated methods that aid in the systematic, continuous, and comprehensive assessment of technological emergence using one of the major foresight exercises, scientometrics. There is now a huge flood of scientific and technical information, especially scientific publications and patent information. Using the information patterns of emergence for technological concepts has been discovered and theories of technical emergence have been also developed in several years. We have been developing visualization tools in which thousands of technical areas have been interacted with each other and evolved in time. Several indicators of technical emergence have been improved by universities, international organizations, and funding agencies. This book intends to provide readers with a comprehensive overview of the current state of the art in scientometrics that focuses on the systematic, continuous, and comprehensive assessment of technological emergence.

Many different people, from social scientists to government agencies to business professionals, depend on the results of multivariate models to inform their decisions. Researchers use these advanced statistical techniques to analyze relationships among multiple variables, such as how exercise and weight relate to the risk of heart disease, or how unemployment and interest rates affect economic growth. Yet, despite the widespread need to plainly and effectively explain the results of multivariate analyses to varied audiences, few are properly taught this critical skill. The Chicago Guide to Writing about Multivariate Analysis is the book researchers turn to when looking for guidance on how to clearly present statistical results and break through the jargon that often clouds writing about applications of statistical analysis. This new edition features even more topics and real-world examples, making it the must-have resource for anyone who needs to communicate complex research results. For this second edition, Jane E. Miller includes four new chapters that cover writing about interactions, writing about event history analysis, writing about multilevel models, and the “Goldilocks principle” for choosing the right size contrast for interpreting results for different variables. In addition, she has updated or added numerous examples, while retaining her clear voice and focus on writers thinking critically about their intended audience and objective. Online podcasts, templates, and an updated study guide will help readers apply skills from the book to their own projects and courses. This continues to be the only book that brings together all of the steps involved in communicating findings based on multivariate analysis—finding data, creating variables, estimating statistical models, calculating overall effects, organizing ideas, designing tables and charts, and writing prose—in a single volume. When aligned with Miller’s twelve fundamental principles for quantitative writing, this approach will empower readers—whether students or experienced researchers—to communicate their findings clearly and effectively.