Certifiable Software Applications 3: Downward Cycle describes the descending phase of the creation of a software application, detailing specification phases, architecture, design and coding, and important concepts on modeling and implementation. For coding, code generation and/or manual code production strategies are explored. As applications are coded, a presentation of programming languages and their impact on certifiability is included.

Research and Evidence in Software Engineering: From Empirical Studies to Open Source Artifacts introduces advanced software engineering to software engineers, scientists, postdoctoral researchers, academicians, software consultants, management executives, doctoral students, and advanced level postgraduate computer science students. This book contains research articles addressing numerous software engineering research challenges associated with various software development-related activities, including programming, testing, measurements, human factors (social software engineering), specification, quality, program analysis, software project management, and more. It provides relevant theoretical frameworks, empirical research findings, and evaluated solutions addressing the research challenges associated with the above-mentioned software engineering activities. To foster collaboration among the software engineering research community, this book also reports datasets acquired systematically through scientific methods and related to various software engineering aspects that are valuable to the research community. These datasets will allow other researchers to use them in their research, thus improving the quality of overall research. The knowledge disseminated by the research studies contained in the book will hopefully motivate other researchers to further innovation in the way software development happens in real practice.

This timely text/reference explores the business and technical issues involved in the management of information systems in the era of big data and beyond. Topics and features: presents review questions and discussion topics in each chapter for classroom group work and individual research assignments; discusses the potential use of a variety of big data tools and techniques in a business environment, explaining how these can fit within an information systems strategy; reviews existing theories and practices in information systems, and explores their continued relevance in the era of big data; describes the key technologies involved in information systems in general and big data in particular, placing these technologies in an historic context; suggests areas for further research in this fast moving domain; equips readers with an understanding of the important aspects of a data scientist's job; provides hands-on experience to further assist in the understanding of the technologies involved.

This book addresses mechanisms for reducing model heterogeneity induced by the absence of explicit semantics expression in the formal techniques used to specify design models. More precisely, it highlights the advances in handling both implicit and explicit semantics in formal system developments, and discusses different contributions expressing different views and perceptions on the implicit and explicit semantics. The book is based on the discussions at the Shonan meeting on this topic held in 2016, and includes contributions from the participants summarising their perspectives on the problem and offering solutions. Divided into 5 parts: domain modelling, knowledge-based modelling, proof-based modelling, assurance cases, and refinement-based modelling, and offers inspiration for researchers and practitioners in the fields of formal methods, system and software engineering, domain knowledge modelling, requirement analysis, and explicit and implicit semantics of modelling languages.

This book presents an exciting new theory of time for a world built on hyper-fast digital media networks. Computers have changed the human social experience enormously. We're becoming familiar with many of the macro changes, but we rarely consider the complex, underlying mechanics of how a technology interacts with our social, political and economic worlds. And we cannot explain how the mechanics of a technology are being translated into social influence unless we understand the role of time in that process. Offering an original reconsideration of temporality, Philip Pond explains how super-powerful computers and global webs of connection have remade time through speed. The book introduces key developments in network time theory and explains their importance, before presenting a new model of time which seeks to reconcile the traditionally separate subjective and objective approaches to time theory and measurement.

This book deals with computer performance by addressing basic preconditions. Besides general considerations about performance, several new approaches are presented. One of them targets memory structures by introducing the possibility of overlapping non-interfering (virtual) address spaces. This approach is based on a newly developed jump transformation between different symbol spaces. Another approach deals with efficiency and accuracy in scientific calculations. Finally the concept of a Neural Relational Data Base Management System is introduced and the performance potential of quantum computers assessed.

This book presents some of the emerging techniques and technologies used to handle Web data management. Authors present novel software architectures and emerging technologies and then validate using experimental data and real world applications. The contents of this book are focused on four popular thematic categories of intelligent Web data management: cloud computing, social networking, monitoring and literature management. The Volume will be a valuable reference to researchers, students and practitioners in the field of Web data management, cloud computing, social networks using advanced intelligence tools.

Why learn functional programming? Isn't that some complicated ivory-tower technique used only in obscure languages like Haskell? In fact, functional programming is actually very simple. It's also very powerful, as Haskell demonstrates by throwing away all the conventional programming tools and using only functional programming features. But it doesn't have to be done that way. Functional programming is a power tool that you can use in addition to all your usual tools, to whatever extent your current mainstream language supports it. Most languages have at least basic support. In this book we use Python and Java and, as a bonus, Scala. If you prefer another language, there will be minor differences in syntax, but the concepts are the same. Give functional programming a try. You may be surprised how much a single power tool can help you in your day-to-day programming.

The book discusses the fundamentals of high-performance computing. The authors combine visualization, comprehensibility, and strictness in their material presentation, and thus influence the reader towards practical application and learning how to solve real computing problems. They address both key approaches to programming modern computing systems: multithreading-based parallelizing in shared memory systems, and applying message-passing technologies in distributed systems. The book is suitable for undergraduate and graduate students, and for researchers and practitioners engaged with high-performance computing systems. Each chapter begins with a theoretical part, where the relevant terminology is introduced along with the basic theoretical results and methods of parallel programming, and concludes with a list of test questions and problems of varying difficulty. The authors include many solutions and hints, and often sample code.

This book details the conceptual foundations, design and implementation of the domain-specific language (DSL) development system DjDSL. DjDSL facilitates design-decision-making on and implementation of reusable DSL and DSL-product lines, and represents the state-of-the-art in language-based and composition-based DSL development. As such, it unites elements at the crossroads between software-language engineering, model-driven software engineering, and feature-oriented software engineering. The book is divided into six chapters. Chapter 1 ("DSL as Variable Software") explains the notion of DSL as variable software in greater detail and introduces readers to the idea of software-product line engineering for DSL-based software systems. Chapter 2 ("Variability Support in DSL Development") sheds light on a number of interrelated dimensions of DSL variability: variable development processes, variable design-decisions, and variability-implementation techniques for DSL. The three subsequent chapters are devoted to the key conceptual and technical contributions of DjDSL: Chapter 3 ("Variable Language Models") explains how to design and implement the abstract syntax of a DSL in a variable manner. Chapter 4 ("Variable Context Conditions") then provides the means to refine an abstract syntax (language model) by using composable context conditions (invariants). Next, Chapter 5 ("Variable Textual Syntaxes") details solutions to implementing variable textual syntaxes for different types of DSL. In closing, Chapter 6 ("A Story of a DSL Family") shows how to develop a mixed DSL in a step-by-step manner, demonstrating how the previously introduced techniques can be employed in an advanced example of developing a DSL family. The book is intended for readers interested in language-oriented as well as model-driven software development, including software-engineering researchers and advanced software developers alike. An understanding of software-engineering basics (architecture, design, implementation, testing) and software patterns is essential. Readers should especially be familiar with the basics of object-oriented modelling (UML, MOF, Ecore) and programming (e.g., Java).

The text focuses on mathematical modeling and applications of advanced techniques of machine learning, and artificial intelligence, including artificial neural networks, evolutionary computing, data mining, and fuzzy systems to solve performance and design issues more precisely. Intelligent computing encompasses technologies, algorithms, and models in providing effective and efficient solutions to a wide range of problems including the airport's intelligent safety system. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in fields including industrial engineering, manufacturing engineering, computer engineering, and mathematics. The book- Discusses mathematical modeling for traffic, sustainable supply chain, vehicular Ad-Hoc networks, internet of things networks with intelligent gateways. Covers advanced machine learning, artificial intelligence, fuzzy systems, evolutionary computing, data mining techniques for real-world problems. Presents applications of mathematical models in chronic diseases such as kidney and coronary artery diseases. Highlights advances in mathematical modeling, strength, and benefits of machine learning and artificial intelligence, including driving goals, applicability, algorithms, and processes involved. Showcases emerging real-life topics on mathematical models, machine learning, and intelligent computing using an interdisciplinary approach. The text presents emerging real-life topics on mathematical models, machine learning, and intelligent computing in a single volume. It will serve as an ideal text for senior undergraduate, graduate students, and researchers in diverse fields domains including industrial and manufacturing engineering, computer engineering, and mathematics.

This book is written for engineering students and working professionals. Technical professionals are increasingly involved in IT issues, such as implementing IT systems, managing them, and taking part in requirements analysis/vendor selection. In this book, the basics of production planning systems (PPS) are covered, as well as their implementation in ERP-Systems like SAP. Readers also learn the basics of practical IT management and software creation through detailed, real-world examples. The book serves as a full 5 ECTS study module, which fits into any engineering curriculum. 150 multiple-choice quizzes, practical exercises and a text filled with experiential examples make it a convenient choice for selfstudy and for classroom use.

Extensive research conducted by the Hasso Plattner Design Thinking Research Program at Stanford University in Palo Alto, California, USA, and the Hasso Plattner Institute in Potsdam, Germany, has yielded valuable insights on why and how design thinking works. The participating researchers have identified metrics, developed models, and conducted studies, which are featured in this book, and in the previous volumes of this series. Offering readers a closer look at design thinking, and its innovation processes and methods, this volume addresses the new and growing field of neurodesign, which applies insights from the neurosciences in order to improve design team performance. Thinking and devising innovations are inherently human activities - and so is design thinking. Accordingly, design thinking is not merely the result of special courses or of being gifted or trained: it is a way of dealing with our environment and improving techniques, technologies and life in general. As such, the research outcomes compiled in this book are intended to inform and provide inspiration for all those seeking to drive innovation - be they experienced design thinkers or newcomers.

Python for Scientific Computation and Artificial Intelligence is split into 3 parts: in Section 1, the reader is introduced to the Python programming language and shown how Python can aid in the understanding of advanced High School Mathematics. In Section 2, the reader is shown how Python can be used to solve real-world problems from a broad range of scientific disciplines. Finally, in Section 3, the reader is introduced to neural networks and shown how TensorFlow (written in Python) can be used to solve a large array of problems in Artificial Intelligence (AI). This book was developed from a series of national and international workshops that the author has been delivering for over twenty years. The book is beginner friendly and has a strong practical emphasis on programming and computational modelling. Features: No prior experience of programming is required. Online GitHub repository available with codes for readers to practice. Covers applications and examples from biology, chemistry, computer science, data science, electrical and mechanical engineering, economics, mathematics, physics, statistics and binary oscillator computing. Full solutions to exercises are available as Jupyter notebooks on the Web.

This volume presents a collection of methods for dealing with software reliability. Ideally, formal methods need to be intuitive to use, require a relatively brief learning period, and incur only small overhead to the development process. This book compares these varying methods and reveals their respective advantages and disadvantages, while also staying close to the dual themes of automata theory and logic. Topics and features: * Collects and compares the key software reliability methods currently in use: deductive verification, automatic verification, testing, and process algebra * Provides useful information suitable in the software selection process for a given project * Offers numerous exercises, projects, and running examples to facilitate learning formal methods and allows for ¿hands-on¿ experience with these critical tools * Describes the mathematical principles supporting formal methods * Gives insights into new research directions in the field, as well as ways of developing new methods and/or adjusting existing ones. This volume can be used as an introduction to software methods techniques, a source for learning about various ways to enhance software reliability, and a guide to formal methods techniques. It is an essential resource for professionals and software engineers in R&D departments in industry, using software reliability, program-modeling systems, and verification methods.

This book covers both theory and applications in the automation of software testing tools and techniques for various types of software (e.g. object-oriented, aspect-oriented, and web-based software). When software fails, it is most often due to lack of proper and thorough testing, an aspect that is even more acute for object-oriented, aspect-oriented, and web-based software. Further, since it is more difficult to test distributed and service-oriented architecture-based applications, there is a pressing need to discuss the latest developments in automated software testing. This book discusses the most relevant issues, models, tools, challenges, and applications in automated software testing. Further, it brings together academic researchers, scientists, and engineers from a wide range of industrial application areas, who present their latest findings and identify future challenges in this fledging research area.

The two-volume set IFIP AICT 639 and 640 constitutes the refereed post-conference proceedings of the 18th IFIP WG 5.1 International Conference on Product Lifecycle Management, PLM 2021, held in Curitiba, Brazil, during July 11-14, 2021. The conference was held virtually due to the COVID-19 crisis.The 107 revised full papers presented in these proceedings were carefully reviewed and selected from 133 submissions. The papers are organized in the following topical sections: Volume I: Sustainability, sustainable development and circular economy; sustainability and information technologies and services; green and blue technologies; AI and blockchain integration with enterprise applications; PLM maturity, PLM implementation and adoption within industry 4.0; and industry 4.0 and emerging technologies: Volume II: Design, education and management; lean, design and innovation technologies; information technology models and design; and models, manufacturing and information technologies and services.

Content-Based Image Classification: Efficient Machine Learning Using Robust Feature Extraction Techniques is a comprehensive guide to research with invaluable image data. Social Science Research Network has revealed that 65% of people are visual learners. Research data provided by Hyerle (2000) has clearly shown 90% of information in the human brain is visual. Thus, it is no wonder that visual information processing in the brain is 60,000 times faster than text-based information (3M Corporation, 2001). Recently, we have witnessed a significant surge in conversing with images due to the popularity of social networking platforms. The other reason for embracing usage of image data is the mass availability of high-resolution cellphone cameras. Wide usage of image data in diversified application areas including medical science, media, sports, remote sensing, and so on, has spurred the need for further research in optimizing archival, maintenance, and retrieval of appropriate image content to leverage data-driven decision-making. This book demonstrates several techniques of image processing to represent image data in a desired format for information identification. It discusses the application of machine learning and deep learning for identifying and categorizing appropriate image data helpful in designing automated decision support systems. The book offers comprehensive coverage of the most essential topics, including: Image feature extraction with novel handcrafted techniques (traditional feature extraction) Image feature extraction with automated techniques (representation learning with CNNs) Significance of fusion-based approaches in enhancing classification accuracy MATLAB (R) codes for implementing the techniques Use of the Open Access data mining tool WEKA for multiple tasks The book is intended for budding researchers, technocrats, engineering students, and machine learning/deep learning enthusiasts who are willing to start their computer vision journey with content-based image recognition. The readers will get a clear picture of the essentials for transforming the image data into valuable means for insight generation. Readers will learn coding techniques necessary to propose novel mechanisms and disruptive approaches. The WEKA guide provided is beneficial for those uncomfortable coding for machine learning algorithms. The WEKA tool assists the learner in implementing machine learning algorithms with the click of a button. Thus, this book will be a stepping-stone for your machine learning journey. Please visit the author's website for any further guidance at https://www.rikdas.com/

This Festschrift, Unimagined Futures - ICT Opportunities and Challenges, is the first Festschrift in the IFIP AICT series. It examines key challenges facing the ICT community today. While addressing the contemporary challenges, the book provides the opportunity to look back to help understand the contemporary scene and identify appropriate future responses to them. Experts in different areas of the ICT scene have contributed to this IFIP 60th anniversary book, which will be a key input to the ICT community worldwide on setting policy priorities and agendas for the coming decade. In addition, a number of contributions look specifically at the role of professionals and of national, regional, and global organizations in disseminating the benefits of ICT to humanity worldwide.

This book presents the state of the art, challenges and future trends in automotive software engineering. The amount of automotive software has grown from just a few lines of code in the 1970s to millions of lines in today's cars. And this trend seems destined to continue in the years to come, considering all the innovations in electric/hybrid, autonomous, and connected cars. Yet there are also concerns related to onboard software, such as security, robustness, and trust. This book covers all essential aspects of the field. After a general introduction to the topic, it addresses automotive software development, automotive software reuse, E/E architectures and safety, C-ITS and security, and future trends. The specific topics discussed include requirements engineering for embedded software systems, tools and methods used in the automotive industry, software product lines, architectural frameworks, various related ISO standards, functional safety and safety cases, cooperative intelligent transportation systems, autonomous vehicles, and security and privacy issues. The intended audience includes researchers from academia who want to learn what the fundamental challenges are and how they are being tackled in the industry, and practitioners looking for cutting-edge academic findings. Although the book is not written as lecture notes, it can also be used in advanced master's-level courses on software and system engineering. The book also includes a number of case studies that can be used for student projects.

This book focuses on a specialized branch of the vast domain of software engineering: component-based software engineering (CBSE). Component-Based Software Engineering: Methods and Metrics enhances the basic understanding of components by defining categories, characteristics, repository, interaction, complexity, and composition. It divides the research domain of CBSE into three major sub-domains: (1) reusability issues, (2) interaction and integration issues, and (3) testing and reliability issues. This book covers the state-of-the-art literature survey of at least 20 years in the domain of reusability, interaction and integration complexities, and testing and reliability issues of component-based software engineering. The aim of this book is not only to review and analyze the previous works conducted by eminent researchers, academicians, and organizations in the context of CBSE, but also suggests innovative, efficient, and better solutions. A rigorous and critical survey of traditional and advanced paradigms of software engineering is provided in the book. Features: In-interactions and Out-Interactions both are covered to assess the complexity. In the context of CBSE both white-box and black-box testing methods and their metrics are described. This work covers reliability estimation using reusability which is an innovative method. Case studies and real-life software examples are used to explore the problems and their solutions. Students, research scholars, software developers, and software designers or individuals interested in software engineering, especially in component-based software engineering, can refer to this book to understand the concepts from scratch. These measures and metrics can be used to estimate the software before the actual coding commences.

This book summarizes the research findings presented at the 13th International Joint Conference on Knowledge-Based Software Engineering (JCKBSE 2020), which took place on August 24-26, 2020. JCKBSE 2020 was originally planned to take place in Larnaca, Cyprus. Unfortunately, the COVID-19 pandemic forced it be rescheduled as an online conference. JCKBSE is a well-established, international, biennial conference that focuses on the applications of artificial intelligence in software engineering. The 2020 edition of the conference was organized by Hiroyuki Nakagawa, Graduate School of Information Science and Technology, Osaka University, Japan, and George A. Tsihrintzis and Maria Virvou, Department of Informatics, University of Piraeus, Greece. This research book is a valuable resource for experts and researchers in the field of (knowledge-based) software engineering, as well as general readers in the fields of artificial and computational Intelligence and, more generally, computer science wanting to learn more about the field of (knowledge-based) software engineering and its applications. An extensive list of bibliographic references at the end of each paper helps readers to probe further into the application areas of interest to them.

Without correct timing, there is no safe and reliable embedded software. This book shows how to consider timing early in the development process for embedded systems, how to solve acute timing problems, how to perform timing optimization, and how to address the aspect of timing verification.The book is organized in twelve chapters. The first three cover various basics of microprocessor technologies and the operating systems used therein. The next four chapters cover timing problems both in theory and practice, covering also various timing analysis techniques as well as special issues like multi- and many-core timing. Chapter 8 deals with aspects of timing optimization, followed by chapter 9 that highlights various methodological issues of the actual development process. Chapter 10 presents timing analysis in AUTOSAR in detail, while chapter 11 focuses on safety aspects and timing verification. Finally, chapter 12 provides an outlook on upcoming and future developments in software timing. The number of embedded systems that we encounter in everyday life is growing steadily. At the same time, the complexity of the software is constantly increasing. This book is mainly written for software developers and project leaders in industry. It is enriched by many practical examples mostly from the automotive domain, yet the vast majority of the book is relevant for any embedded software project. This way it is also well-suited as a textbook for academic courses with a strong practical emphasis, e.g. at applied sciences universities. Features and Benefits * Shows how to consider timing in the development process for embedded systems, how to solve timing problems, and how to address timing verification * Enriched by many practical examples mostly from the automotive domain * Mainly written for software developers and project leaders in industry

How to Find and Fix the Killer Software Bugs that Evade Conventional Testing In Exploratory Software Testing, renowned software testing expert James Whittaker reveals the real causes of today's most serious, well-hidden software bugs--and introduces powerful new "exploratory" techniques for finding and correcting them. Drawing on nearly two decades of experience working at the cutting edge of testing with Google, Microsoft, and other top software organizations, Whittaker introduces innovative new processes for manual testing that are repeatable, prescriptive, teachable, and extremely effective. Whittaker defines both in-the-small techniques for individual testers and in-the-large techniques to supercharge test teams. He also introduces a hybrid strategy for injecting exploratory concepts into traditional scripted testing. You'll learn when to use each, and how to use them all successfully. Concise, entertaining, and actionable, this book introduces robust techniques that have been used extensively by real testers on shipping software, illuminating their actual experiences with these techniques, and the results they've achieved. Writing for testers, QA specialists, developers, program managers, and architects alike, Whittaker answers crucial questions such as: * Why do some bugs remain invisible to automated testing--and how can I uncover them? * What techniques will help me consistently discover and eliminate "show stopper" bugs? * How do I make manual testing more effective--and less boring and unpleasant? * What's the most effective high-level test strategy for each project? * Which inputs should I test when I can't test them all? * Which test cases will provide the best feature coverage? * How can I get better results by combining exploratory testing with traditional script or scenario-based testing? * How do I reflect feedback from the development process, such as code changes?