This book brings together two important trends: graph algorithms and high-performance computing. Efficient and scalable execution of graph processing applications in data or network analysis requires innovations at multiple levels: algorithms, associated data structures, their implementation and tuning to a particular hardware. Further, programming languages and the associated compilers play a crucial role when it comes to automating efficient code generation for various architectures. This book discusses the essentials of all these aspects. The book is divided into three parts: programming, languages, and their compilation. The first part examines the manual parallelization of graph algorithms, revealing various parallelization patterns encountered, especially when dealing with graphs. The second part uses these patterns to provide language constructs that allow a graph algorithm to be specified. Programmers can work with these language constructs without worrying about their implementation, which is the focus of the third part. Implementation is handled by a compiler, which can specialize code generation for a backend device. The book also includes suggestive results on different platforms, which illustrate and justify the theory and practice covered. Together, the three parts provide the essential ingredients for creating a high-performance graph application. The book ends with a section on future directions, which offers several pointers to promising topics for future research. This book is intended for new researchers as well as graduate and advanced undergraduate students. Most of the chapters can be read independently by those familiar with the basics of parallel programming and graph algorithms. However, to make the material more accessible, the book includes a brief background on elementary graph algorithms, parallel computing and GPUs. Moreover it presents a case study using Falcon, a domain-specific language for graph algorithms, to illustrate the concepts.

This book discusses important topics for engineering and managing software startups, such as how technical and business aspects are related, which complications may arise and how they can be dealt with. It also addresses the use of scientific, engineering, and managerial approaches to successfully develop software products in startup companies. The book covers a wide range of software startup phenomena, and includes the knowledge, skills, and capabilities required for startup product development; team capacity and team roles; technical debt; minimal viable products; startup metrics; common pitfalls and patterns observed; as well as lessons learned from startups in Finland, Norway, Brazil, Russia and USA. All results are based on empirical findings, and the claims are backed by evidence and concrete observations, measurements and experiments from qualitative and quantitative research, as is common in empirical software engineering. The book helps entrepreneurs and practitioners to become aware of various phenomena, challenges, and practices that occur in real-world startups, and provides insights based on sound research methodologies presented in a simple and easy-to-read manner. It also allows students in business and engineering programs to learn about the important engineering concepts and technical building blocks of a software startup. It is also suitable for researchers at different levels in areas such as software and systems engineering, or information systems who are studying advanced topics related to software business.

This book briefly summarizes the current state of the art technologies and solutions for location and tracking (L&T) in wireless sensor networks (WSN), focusing on RSS-based schemes. The authors offer broad and in-depth coverage of essential topics including range-based and range-free localization strategies, and signal path loss models. In addition, the book includes motion models and how state estimation techniques and advanced machine learning techniques can be utilized to design L&T systems for a given problem using low cost measurement metric (that is RSS). This book also provides MATLAB examples to demonstrate fundamental algorithms for L&T and provides online access to all MATLAB codes. The book allows practicing engineers and graduate students to keep pace with contemporary research and new technologies in the L&T domain.

User-Developer Cooperation in Software Development brings together the strengths of task analysis and user participation within an overall software development process, and presents a detailed observation and theoretical analysis of what it is for users and developers to cooperate, and the nature of user-developer interaction. Eamonn O'Neill deals with these issues through the development and application of an approach to task-based participatory development in two real world development projects, and discusses the strengths of task analysis and participatory design methods, and how they complement each other's weaker aspects.

This book is designed for those who manage software development projects. It explores software and risk management both from a technology and a business perspective. Issues regarding costs, schedules, technical performance, and strategies for software development are discussed.
The author approaches software development from a just-in-time viewpoint and details strategies for implementing and planning development plans in a cost-effective and timely manner. The book presents a significant discussion of software risk issues pertaining to organizational costs and schedules. It also identifies metrics and presents several models for measuring and predicting risk.
The information featured in the book is supported by actual proven case studies derived from the author's experience. The text addresses many different concepts, strategies, and tools that could make the management of your next software development project less of a guess and more predictable.
Also available is the SERIM Software Tool. This interactive, easy-to-use Windows application gives you an automated way to determine the risks of your software project. The product is based on the SERIM model detailed in this bestselling book.

Software maintenance work is often considered a dauntingly rigid activity - this book proves the opposite: it demands high levels of creativity and thinking outside the box. Highlighting the creative aspects of software maintenance and combining analytical and systems thinking in a holistic manner, the book motivates readers not to blithely follow the beaten tracks of "technical rationality". It delivers the content in a pragmatic fashion using case studies which are woven into long running story lines. The book is organized in four parts, which can be read in any order, except for the first chapter, which introduces software maintenance and evolution and presents a number of case studies of software failures. The "Introduction to Key Concepts" briefly introduces the major elements of software maintenance by highlighting various core concepts that are vital in order to see the forest for the trees. Each such concept is illustrated with a worked example. Next, the "Forward Engineering" part debunks the myth that being fast and successful during initial development is all that matters. To this end, two categories of forward engineering are considered: an inept initial project with a multitude of hard evolutionary phases and an effective initial project with multiple straightforward future increments. "Reengineering and Reverse Engineering" shows the difficulties of dealing with a typical legacy system, and tackles tasks such as retrofitting tests, documenting a system, restructuring a system to make it amenable for further improvements, etc. Lastly, the "DevOps" section focuses on the importance and benefits of crossing the development versus operation chasm and demonstrates how the DevOps paradigm can turn a loosely coupled design into a loosely deployable solution. The book is a valuable resource for readers familiar with the Java programming language, and with a basic understanding and/or experience of software construction and testing. Packed with examples for every elaborated concept, it offers complementary material for existing courses and is useful for students and professionals alike.

A Tour of Data Science: Learn R and Python in Parallel covers the fundamentals of data science, including programming, statistics, optimization, and machine learning in a single short book. It does not cover everything, but rather, teaches the key concepts and topics in Data Science. It also covers two of the most popular programming languages used in Data Science, R and Python, in one source. Key features: Allows you to learn R and Python in parallel Cover statistics, programming, optimization and predictive modelling, and the popular data manipulation tools - data.table and pandas Provides a concise and accessible presentation Includes machine learning algorithms implemented from scratch, linear regression, lasso, ridge, logistic regression, gradient boosting trees, etc. Appealing to data scientists, statisticians, quantitative analysts, and others who want to learn programming with R and Python from a data science perspective.

This book covers a broad range of topics related to digitalization. Specifically, it views digitalization across different organizational levels, such as the level of individuals, teams, processes, firms, and ecosystems. It includes a collection of recent research and reflections on the topic that helps to understand the technological foundations of digitalization and its impacts. It also reflects on the process of digitalization and how it changes established ways of working, collaborating, and coordinating. With this book, the editors and authors honor Professor Dr. Armin Heinzl for his enormous and ongoing contributions to information systems research, education, and practice.

This book constitutes the refereed proceedings of the 23rd IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2022, held in Lisbon, Portugal, in September 2022. The 55 papers presented were carefully reviewed and selected from 119 submissions. They provide a comprehensive overview of major challenges and recent advances in various domains related to the digital transformation and collaborative networks and their applications with a strong focus on the following areas related to the main theme of the conference: sustainable collaborative networks; sustainability via digitalization; analysis and assessment of business ecosystems; human factors in collaboration 4.0; maintenance and life-cycle management; policies and new digital services; safety and collaboration management; simulation and optimization; complex collaborative systems and ontologies; value co-creation in digitally enabled ecosystems; digitalization strategy in collaborative enterprises' networks; pathways and tools for DIHs; socio-technical perspectives on smart product-service systems; knowledge transfer and accelerated innovation in FoF; interoperability of IoT and CPS for industrial CNs; sentient immersive response network; digital tools and applications for collaborative healthcare; collaborative networks and open innovation in education 4.0; collaborative learning networks with industry and academia; and industrial workshop.

Demonstrates how category theory can be used for formal software development.

The mathematical toolbox for the Software Engineering in the new age of complex interactive systems.

This book focuses on software reuse and the chances, dependability tests and recommendations for best reuse practice. A short introduction of the Ecodesign of hardware is given combined with the latest update of relevant EU legislation and standardization. It also describes the combination of different states of software in a E&E system in order to guarantee dependability of the product to be resold.

Open-source development has been around for decades, with software developers co-creating tools and information systems for widespread use. With the development of open-source software such as learning objects, interactive articles, and educational games, the open-source values and practices have slowly been adopted by those in education sectors. Open-Source Technologies for Maximizing the Creation, Deployment, and Use of Digital Resources and Information highlights the global importance of open-source technologies in higher and general education. Written for those working in education and professional training, this collection of research explores a variety of issues related to open-source in education, such as its practical underpinnings, requisite cultural competence in global open-source, strategies for employing open-source in online learning and research, the design of an open-source networking laboratory, and other endeavors.

This book presents a new paradigm of software testing by emphasizing the role of critical thinking, system thinking and rationality as the most important skills for the tester. It thus approaches software testing from a different perspective than in past literature, as the vast majority of books describe testing in the context of specific tools, automation, documentation, particular test design techniques or test management. In addition, the book proposes a novel meta-approach for designing effective test strategies, which is based on recent advances in psychology, economics, system sciences and logic. Chapter 1 starts by introducing the fundamental ideas underlying software testing. Chapter 2 then describes meta-strategies in software testing, i.e. general approaches that can be adapted to many different situations that a software tester encounters. Next, Chapter 3 presents the concept of Thinking-Driven Testing (TDT). This approach utilizes the concepts discussed in the two previous chapters and introduces the main ideas that underlie a reasonable and optimal approach to software testing. Chapter 4 builds on this basis and proposes a specific approach to testing, called TQED, that makes it possible to increase creativity in the context of delivering effective, optimal test ideas. Chapter 5 provides an overview of different types of testing techniques in order to understand the fundamental concepts of test design, while Chapter 6 details various pitfalls a tester may encounter and that can originate from a wide range of testing process areas. Lastly, Chapter 7 puts all this into practice, as it contains several exercises that will help testers develop a number of crucial skills: logical thinking and reasoning, thinking out of the box, creativity, counting and estimating, and analytical thinking. By promoting critical, rational and creative thinking, this book invites readers to re-examine common assumptions regarding software testing and shows them how to become professional testers who bring added value to their company.

The first edition of Exercises in Programming Style was honored as an ACM Notable Book and praised as "The best programming book of the decade." This new edition retains the same presentation but has been upgraded to Python 3, and there is a new section on neural network styles. Using a simple computational task (term frequency) to illustrate different programming styles, Exercises in Programming Style helps readers understand the various ways of writing programs and designing systems. It is designed to be used in conjunction with code provided on an online repository. The book complements and explains the raw code in a way that is accessible to anyone who regularly practices the art of programming. The book can also be used in advanced programming courses in computer science and software engineering programs. The book contains 40 different styles for writing the term frequency task. The styles are grouped into ten categories: historical, basic, function composition, objects and object interactions, reflection and metaprogramming, adversity, data-centric, concurrency, interactivity, and neural networks. The author states the constraints in each style and explains the example programs. Each chapter first presents the constraints of the style, next shows an example program, and then gives a detailed explanation of the code. Most chapters also have sections focusing on the use of the style in systems design as well as sections describing the historical context in which the programming style emerged.

This book covers several topics related to domain-specific language (DSL) engineering in general and how they can be handled by means of the JetBrains Meta Programming System (MPS), an open source language workbench developed by JetBrains over the last 15 years. The book begins with an overview of the domain of language workbenches, which provides perspectives and motivations underpinning the creation of MPS. Moreover, technical details of the language underneath MPS together with the definition of the tool's main features are discussed. The remaining ten chapters are then organized in three parts, each dedicated to a specific aspect of the topic. Part I "MPS in Industrial Applications" deals with the challenges and inadequacies of general-purpose languages used in companies, as opposed to the reasons why DSLs are essential, together with their benefits and efficiency, and summarizes lessons learnt by using MPS. Part II about "MPS in Research Projects" covers the benefits of text-based languages, the design and development of gamification applications, and research fields with generally low expertise in language engineering. Eventually, Part III focuses on "Teaching and Learning with MPS" by discussing the organization of both commercial and academic courses on MPS. MPS is used to implement languages for real-world use. Its distinguishing feature is projectional editing, which supports practically unlimited language extension and composition possibilities as well as a flexible mix of a wide range of textual, tabular, mathematical and graphical notations. The number and diversity of the presented use-cases demonstrate the strength and malleability of the DSLs defined using MPS. The selected contributions represent the current state of the art and practice in using JetBrains MPS to implement languages for real-world applications.

Advances in Computers, Volume 118, the latest volume in this innovative series published since 1960, presents detailed coverage of new advancements in computer hardware, software, theory, design and applications. Chapters in this updated release include Introduction to non-volatile memory technologies, The emerging phase-change memory, Phase-change memory architectures, Inter-line level schemes for handling hard errors in PCMs, Handling hard errors in PCMs by using intra-line level schemes, and Addressing issues with MLC Phase-change Memory.

This textbook sets out to provide professionals with an in-depth understanding of the software-testing people and process issues that are critical for delivering high-quality software on time and within budget. The authors aim to give those involved in building and maintaining complex, mission-critical software systems a flexible, risk-based process to improve their software-testing capabilities. Whether an organization currently has a well-defined testing process or almost no process, this resource provides insights into better ways to test software. This guide is written for: software-test managers; testers; developers; quality-assurance managers; and software configuration managers.

In an article for Wired Magazine in 2006, Jeff Howe defined crowdsourcing as an idea for outsourcing a task that is traditionally performed by a single employee to a large group of people in the form of an open call. Since then, by modifying crowdsourcing into different forms, some of the most successful new companies on the market have used this idea to make people's lives easier and better. On the other hand, software testing has long been recognized as a time-consuming and expensive activity. Mobile application testing is especially difficult, largely due to compatibility issues: a mobile application must work on devices with different operating systems (e.g. iOS, Android), manufacturers (e.g. Huawei, Samsung) and keypad types (e.g. virtual keypad, hard keypad). One cannot be 100% sure that, just because a tested application works well on one device, it will run smoothly on all others.Crowdsourced testing is an emerging paradigm that can improve the cost-effectiveness of software testing and accelerate the process, especially for mobile applications. It entrusts testing tasks to online crowdworkers whose diverse testing devices/contexts, experience, and skill sets can significantly contribute to more reliable, cost-effective and efficient testing results. It has already been adopted by many software organizations, including Google, Facebook, Amazon and Microsoft. This book provides an intelligent overview of crowdsourced testing research and practice. It employs machine learning, data mining, and deep learning techniques to process the data generated during the crowdsourced testing process, to facilitate the management of crowdsourced testing, and to improve the quality of crowdsourced testing.

Activity theory is a way of describing and characterizing the structure of human - tivity of all kinds. First introduced by Russian psychologists Rubinshtein, Leontiev, and Vigotsky in the early part of the last century, activity theory has more recently gained increasing attention among interaction designers and others in the hum- computer interaction and usability communities (see, for example, Gay and H- brooke, 2004). Interest was given a signi?cant boost when Donald Norman suggested activity-theory and activity-centered design as antidotes to some of the putative ills of "human-centered design" (Norman, 2005). Norman, who has been credited with coining the phrase "user-centered design," suggested that too much attention focused on human users may be harmful, that to design better tools designers need to focus not so much on users as on the activities in which users are engaged and the tasks they seek to perform within those activities. Although many researchers and practitioners claim to have used or been in?uenced by activity theory in their work (see, for example, Nardi, 1996), it is often dif?cult to trace precisely where or how the results have actually been shaped by activity theory. Inmanycases, evendetailedcasestudiesreportresultsthatseemonlydistantlyrelated, if at all, to the use of activity theory. Contributing to the lack of precise and traceable impact is that activity theory, - spite its name, is not truly a formal and proper theory.

Awareness of design smells - indicators of common design problems - helps developers or software engineers understand mistakes made while designing, what design principles were overlooked or misapplied, and what principles need to be applied properly to address those smells through refactoring. Developers and software engineers may "know" principles and patterns, but are not aware of the "smells" that exist in their design because of wrong or mis-application of principles or patterns. These smells tend to contribute heavily to technical debt - further time owed to fix projects thought to be complete - and need to be addressed via proper refactoring. Refactoring for Software Design Smells presents 25 structural design smells, their role in identifying design issues, and potential refactoring solutions. Organized across common areas of software design, each smell is presented with diagrams and examples illustrating the poor design practices and the problems that result, creating a catalog of nuggets of readily usable information that developers or engineers can apply in their projects. The authors distill their research and experience as consultants and trainers, providing insights that have been used to improve refactoring and reduce the time and costs of managing software projects. Along the way they recount anecdotes from actual projects on which the relevant smell helped address a design issue.

Combinatorial optimisation is a ubiquitous discipline whose usefulness spans vast applications domains. The intrinsic complexity of most combinatorial optimisation problems makes classical methods unaffordable in many cases. To acquire practical solutions to these problems requires the use of metaheuristic approaches that trade completeness for pragmatic effectiveness. Such approaches are able to provide optimal or quasi-optimal solutions to a plethora of difficult combinatorial optimisation problems.

The application of metaheuristics to combinatorial optimisation is an active field in which new theoretical developments, new algorithmic models, and new application areas are continuously emerging. This volume presents recent advances in the area of metaheuristic combinatorial optimisation, with a special focus on evolutionary computation methods. Moreover, it addresses local search methods and hybrid approaches. In this sense, the book includes cutting-edge theoretical, methodological, algorithmic and applied developments in the field, from respected experts and with a sound perspective.

This book presents in their basic form the most important models of computation, their basic programming paradigms, and their mathematical descriptions, both concrete and abstract. Each model is accompanied by relevant formal techniques for reasoning on it and for proving some properties. After preliminary chapters that introduce the notions of structure and meaning, semantic methods, inference rules, and logic programming, the authors arrange their chapters into parts on IMP, a simple imperative language; HOFL, a higher-order functional language; concurrent, nondeterministic and interactive models; and probabilistic/stochastic models. The authors have class-tested the book content over many years, and it will be valuable for graduate and advanced undergraduate students of theoretical computer science and distributed systems, and for researchers in this domain. Each chapter of the book concludes with a list of exercises addressing the key techniques introduced, solutions to selected exercises are offered at the end of the book.

This book constitutes the refereed proceedings of five International Workshops held as parallel events of the 18th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2022, virtually and in Hersonissos, Crete, Greece, in June 2022: the 11th Mining Humanistic Data Workshop (MHDW 2022); the 7th 5G-Putting Intelligence to the Network Edge Workshop (5G-PINE 2022); the 1st workshop on AI in Energy, Building and Micro-Grids (AIBMG 2022); the 1st Workshop/Special Session on Machine Learning and Big Data in Health Care (ML@HC 2022); and the 2nd Workshop on Artificial Intelligence in Biomedical Engineering and Informatics (AIBEI 2022). The 35 full papers presented at these workshops were carefully reviewed and selected from 74 submissions.