In his latest work, author Paul C Jorgensen takes his well-honed craftsman's approach to mastering model-based testing (MBT). To be expert at MBT, a software tester has to understand it as a craft rather than an art. This means a tester should have deep knowledge of the underlying subject and be well practiced in carrying out modeling and testing techniques. Judgment is needed, as well as an understanding of MBT the tools. The first part of the book helps testers in developing that judgment. It starts with an overview of MBT and follows with an in-depth treatment of nine different testing models with a chapter dedicated to each model. These chapters are tied together by a pair of examples: a simple insurance premium calculation and an event-driven system that describes a garage door controller. The book shows how simpler models-flowcharts, decision tables, and UML Activity charts-express the important aspects of the insurance premium problem. It also shows how transition-based models-finite state machines, Petri nets, and statecharts-are necessary for the garage door controller but are overkill for the insurance premium problem. Each chapter describes the extent to which a model can support MBT. The second part of the book gives testers a greater understanding of MBT tools. It examines six commercial MBT products, presents the salient features of each product, and demonstrates using the product on the insurance premium and the garage door controller problems. These chapters each conclude with advice on implementing MBT in an organization. The last chapter describes six Open Source tools to round out a tester's knowledge of MBT. In addition, the book supports the International Software Testing Qualifications Board's (ISTQB (R)) MBT syllabus for certification.

Maintaining the advanced technical focus found in Developing Essbase Applications, this second volume is another collaborative effort by some of the best and most experienced Essbase practitioners from around the world. Developing Essbase Applications: Hybrid Techniques and Practices reviews technology areas that are much-discussed but still very new, including Exalytics and Hybrid Essbase. Covering recent improvements to the Essbase engine, the book illustrates the impact of new reporting and analysis tools and also introduces advanced Essbase best practices across a variety of features, functions, and theories. Some of this book's chapters are in the same vein as the previous volume: hardware, engines, and languages. Others cover new ground with Oracle Business Intelligence Enterprise Edition, design philosophy, benchmarking concepts, and multiple client tools. As before, these subjects are covered from both the technical and best practice perspectives. This updated volume continues in the tradition of its bestselling predecessor by defining, investigating, and explaining Essbase concepts like no other resource. It also includes use cases that transform abstract theory into practical examples you can easily relate to your own Essbase environment. Illustrating the recent expansion of Essbase functionality, this book provides the up-to-date understanding you need to explore the full depth of the Essbase technology stack. Although the book presents detailed tutorial chapters that can be read on their own, reading the entire book will provide you with a similar understanding as some of the most experienced Essbase practitioners from around the world.

Software development has been a troubling since it first started. There are seven chronic problems that have plagued it from the beginning: Incomplete and ambiguous user requirements that grow by >2% per month. Major cost and schedule overruns for large applications > 35% higher than planned. Low defect removal efficiency (DRE) < 85% on large systems. Cancelled projects that are not completed: > 30% above 10,000 function points. Poor quality and low reliability after the software is delivered: > 5 bugs per FP. Breach of contract litigation against software outsource vendors. Expensive maintenance and enhancement costs after delivery. These are endemic problems for software executives, software engineers and software customers but they are not insurmountable. In Software Development Patterns and Antipatterns, software engineering and metrics pioneer Capers Jones presents technical solutions for all seven. The solutions involve moving from harmful patterns of software development to effective patterns of software development. The first section of the book examines common software development problems that have been observed in many companies and government agencies. The data on the problems comes from consulting studies, breach of contract lawsuits, and the literature on major software failures. This section considers the factors involved with cost overruns, schedule delays, canceled projects, poor quality, and expensive maintenance after deployment. The second section shows patterns that lead to software success. The data comes from actual companies. The section's first chapter on Corporate Software Risk Reduction in a Fortune 500 company was based on a major telecom company whose CEO was troubled by repeated software failures. The other chapters in this section deal with methods of achieving excellence, as well as measures that can prove excellence to C-level executives, and with continuing excellence through the maintenance cycle as well as for software development.

The purpose of this book is to develop capacity building in strategic and non-strategic machine tool technology. The book contains chapters on how to functionally reverse engineer strategic and non-strategic computer numerical control machinery. Numerous engineering areas, such as mechanical engineering, electrical engineering, control engineering, and computer hardware and software engineering, are covered. The book offers guidelines and covers design for machine tools, prototyping, augmented reality for machine tools, modern communication strategies, and enterprises of functional reverse engineering, along with case studies. Features Presents capacity building in machine tool development Discusses engineering design for machine tools Covers prototyping of strategic and non-strategic machine tools Illustrates augmented reality for machine tools Includes Internet of Things (IoT) for machine tools

Fast Python for Data Science is a hands-on guide to writing Python code that can process more data, faster, and with less resources. It takes a holistic approach to Python performance, showing you how your code, libraries, and computing architecture interact and can be optimized together. Written for experienced practitioners, Fast Python for Data Science dives right into practical solutions for improving computation and storage efficiency. You'll experiment with fun and interesting examples such as rewriting games in lower-level Cython and implementing a MapReduce framework from scratch. Finally, you'll go deep into Python GPU computing and learn how modern hardware has rehabilitated some former antipatterns and made counterintuitive ideas the most efficient way of working. About the technology Fast, accurate systems are vital for handling the huge datasets and complex analytical algorithms that are common in modern data science. Python programmers need to boost performance by writing faster pure-Python programs, optimizing the use of libraries, and utilizing modern multi-processor hardware; Fast Python for Data Science shows you how.

Software development has been a troubling since it first started. There are seven chronic problems that have plagued it from the beginning: Incomplete and ambiguous user requirements that grow by >2% per month. Major cost and schedule overruns for large applications > 35% higher than planned. Low defect removal efficiency (DRE) < 85% on large systems. Cancelled projects that are not completed: > 30% above 10,000 function points. Poor quality and low reliability after the software is delivered: > 5 bugs per FP. Breach of contract litigation against software outsource vendors. Expensive maintenance and enhancement costs after delivery. These are endemic problems for software executives, software engineers and software customers but they are not insurmountable. In Software Development Patterns and Antipatterns, software engineering and metrics pioneer Capers Jones presents technical solutions for all seven. The solutions involve moving from harmful patterns of software development to effective patterns of software development. The first section of the book examines common software development problems that have been observed in many companies and government agencies. The data on the problems comes from consulting studies, breach of contract lawsuits, and the literature on major software failures. This section considers the factors involved with cost overruns, schedule delays, canceled projects, poor quality, and expensive maintenance after deployment. The second section shows patterns that lead to software success. The data comes from actual companies. The section's first chapter on Corporate Software Risk Reduction in a Fortune 500 company was based on a major telecom company whose CEO was troubled by repeated software failures. The other chapters in this section deal with methods of achieving excellence, as well as measures that can prove excellence to C-level executives, and with continuing excellence through the maintenance cycle as well as for software development.

Based on the practical experiences of its authors, who collectively have spent several decades teaching software skills to scientists. Readers only need a basic understanding of Python includes over a hundred exercises to allow readers to practice their skills

New computational design tools have evolved rapidly and been increasingly applied in the field of design in recent years, complimenting and even replacing the traditional design media and approaches. Design as both the process and product are changing due to the emergence and adoption of these new technologies. Understanding and assessing the impact of these new computational design environments on design and designers is important for advancing design in the contemporary context. Do these new computational environments support or hinder design creativity? How do those tools facilitate designers' thinking? Such knowledge is also important for the future development of design technologies. Research shows that design is never a mysterious non-understandable process, for example, one general view is that design process shares a common analysis-synthesis-evaluation model, during which designers interact between design problem and solution spaces. Understanding designers' thinking in different environments is the key to design research, education and practice. This book focuses on emerging computational design environments, whose impact on design and designers have not been comprehensively and systematically studied. It comprises three parts. The history and recent developments of computational design technologies are introduced in Part I. The main categories of technologies cover from computer-aided drafting and modelling tools, to visual programming and scripting tools for algorithmic design, to advanced interfaces and platforms for interactions between designers, between designers and computers, and between the virtual environment and the physical reality. To critically explore design thinking, especially in these new computational design environments, formal approaches to studying design thinking and design cognition are introduced and compared in Part II, drawing on literature and studies from the 70s to the current era. Part III concludes the book by exploring the impact of different computational design technologies on design and designers, using a series of case studies conducted by the author team building on their close collaboration over the past five years. The book offers new insights into designers' thinking in the rapidly evolving computational design environments, which have not been critically and systematically studied and reported in the current literature. The book is meant for design researchers, educators and students, professional practitioners and consultants, as well as people who are interested in computational design in general.

Based on the practical experiences of its authors, who collectively have spent several decades teaching software skills to scientists. Readers only need a basic understanding of Python includes over a hundred exercises to allow readers to practice their skills

In modern conditions of the development of intelligent systems to solve the problems of smart cities, more and more attention is paid to the construction of distributed intelligent systems, which, based on a network of sensors and specialized calculators, help residents and visitors of the city in real time to solve a whole range of complex problems that arise in an urban environment. In a smart city, much attention is paid to the processing of biometric information that comes from biometric sensors distributed throughout the city. Such biometric systems are multimodal and allow you to control the general condition of a person, and also help a person to move around the city and predict events within the city. This book describes methods for processing biometric information in a smart city environment. The theoretical foundations of building a biometric multisensor network, which allows you to create a unified urban biometric community, are considered. The theoretical foundations of the parallel shift technology and the Radon transformation on cellular automata with a hexagonal covering are presented. On the basis of these technologies, methods of biometric identification by gait parameters and the geometric shape of the auricle are described, which are effectively used in a smart city. A method for tracking dynamic changes in the state of a smart city in real time is considered. Models of behavior of colonies of living organisms, their formation, movement and interaction are described on the basis of the technology of cellular automata with active cells. Models of behavior of active cells in meeting with unwanted cells and models of combining and destruction of active cell colonies are also described. This book is intended for undergraduate, graduate students and specialists working and conducting research in the field of biometric information processing, as well as in the development and construction of distributed intelligent systems.

Software Engineering: A Methodical Approach (Second Edition) provides a comprehensive, but concise introduction to software engineering. It adopts a methodical approach to solving software engineering problems, proven over several years of teaching, with outstanding results. The book covers concepts, principles, design, construction, implementation, and management issues of software engineering. Each chapter is organized systematically into brief, reader-friendly sections, with itemization of the important points to be remembered. Diagrams and illustrations also sum up the salient points to enhance learning. Additionally, the book includes the author's original methodologies that add clarity and creativity to the software engineering experience. New in the Second Edition are chapters on software engineering projects, management support systems, software engineering frameworks and patterns as a significant building block for the design and construction of contemporary software systems, and emerging software engineering frontiers. The text starts with an introduction of software engineering and the role of the software engineer. The following chapters examine in-depth software analysis, design, development, implementation, and management. Covering object-oriented methodologies and the principles of object-oriented information engineering, the book reinforces an object-oriented approach to the early phases of the software development life cycle. It covers various diagramming techniques and emphasizes object classification and object behavior. The text features comprehensive treatments of: Project management aids that are commonly used in software engineering An overview of the software design phase, including a discussion of the software design process, design strategies, architectural design, interface design, database design, and design and development standards User interface design Operations design Design considerations including system catalog, product documentation, user message management, design for real-time software, design for reuse, system security, and the agile effect Human resource management from a software engineering perspective Software economics Software implementation issues that range from operating environments to the marketing of software Software maintenance, legacy systems, and re-engineering This textbook can be used as a one-semester or two-semester course in software engineering, augmented with an appropriate CASE or RAD tool. It emphasizes a practical, methodical approach to software engineering, avoiding an overkill of theoretical calculations where possible. The primary objective is to help students gain a solid grasp of the activities in the software development life cycle to be confident about taking on new software engineering projects.

- The first comprehensive beginner's guide to the Grease Pencil component of Blender - Facets of operation are explained in short concise chapters with cross references - Written instruction is accompanied by diagram illustrations, in reference to the program's Graphical User Interface

"... an engaging book that will empower readers in both large and small software development and engineering organizations to build security into their products. ... Readers are armed with firm solutions for the fight against cyber threats."-Dr. Dena Haritos Tsamitis. Carnegie Mellon University"... a must read for security specialists, software developers and software engineers. ... should be part of every security professional's library." -Dr. Larry Ponemon, Ponemon Institute"... the definitive how-to guide for software security professionals. Dr. Ransome, Anmol Misra, and Brook Schoenfield deftly outline the procedures and policies needed to integrate real security into the software development process. ...A must-have for anyone on the front lines of the Cyber War ..." -Cedric Leighton, Colonel, USAF (Ret.), Cedric Leighton Associates"Dr. Ransome, Anmol Misra, and Brook Schoenfield give you a magic formula in this book - the methodology and process to build security into the entire software development life cycle so that the software is secured at the source! "-Eric S. Yuan, Zoom Video CommunicationsThere is much publicity regarding network security, but the real cyber Achilles' heel is insecure software. Millions of software vulnerabilities create a cyber house of cards, in which we conduct our digital lives. In response, security people build ever more elaborate cyber fortresses to protect this vulnerable software. Despite their efforts, cyber fortifications consistently fail to protect our digital treasures. Why? The security industry has failed to engage fully with the creative, innovative people who write software. Core Software Security expounds developer-centric software security, a holistic process to engage creativity for security. As long as software is developed by humans, it requires the human element to fix it. Developer-centric security is not only feasible but also cost effective and operationally relevant. The methodology builds security into software development, which lies at the heart of our cyber infrastructure. Whatever development method is employed, software must be secured at the source. Book Highlights: Supplies a practitioner's view of the SDL Considers Agile as a security enabler Covers the privacy elements in an SDL Outlines a holistic business-savvy SDL framework that includes people, process, and technology Highlights the key success factors, deliverables, and metrics for each phase of the SDL Examines cost efficiencies, optimized performance, and organizational structure of a developer-centric software security program and PSIRT Includes a chapter by noted security architect Brook Schoenfield who shares his insights and experiences in applying the book's SDL framework View the authors' website at http://www.androidinsecurity.com/

Microservices architecture (MSA) is increasingly popular with software architects and engineers as it accelerates software solution design, development, and deployment in a risk-free manner. Placing a software system into a production environment is elegantly simplified and sped up with the use of MSA development platforms, runtime environments, acceleration engines, design patterns, integrated frameworks, and related tools. The MSA ecosystem is expanding with third-party products that automate as many tasks as possible. MSA is being positioned as the enterprise-grade and agile-application design method. This book covers in-depth the features and facilities that make up the MSA ecosystem. Beginning with an overview of Service-Oriented Architecture (SOA) that covers the Common Object Request Broker Architecture (CORBA), Distributed Component Object Model (DCOM), and Remote Method Invocation (RMI), the book explains the basic essentials of MSA and the continuous delivery of applications to customers. The book gives software developers insight into: Current and emerging communication models Key architectural elements of MSA-based applications Designing efficient APIs for microservices MSA middleware platforms such as REST, SOAP, Apache Thrift, and gRPC Microservice discovery and the API gateway Service orchestration and choreography for composing individual services to achieve a useful business process Database transactions in MSA-centric applications Design, composition, security, and deployment patterns MSA security Modernizing legacy applications The book concludes with a chapter on composing and building powerful microservices. With the exponential growth of IoT devices, microservices are being developed and deployed on resource-constrained but resource-intensive devices in order to provide people-centric applications. The book discusses the challenges of these applications. Finally, the book looks at the role of microservices in smart environments and upcoming trends including ubiquitous yet disappearing microservices.

Digitising Enterprise in an Information Age is an effort that focuses on a very vast cluster of Enterprises and their digitising technology involvement and take us through the road map of the implementation process in them, some of them being ICT, Banking, Stock Markets, Textile Industry & ICT, Social Media, Software Quality Assurance, Information Systems Security and Risk Management, Employee Resource Planning etc. It delves on increased instances of cyber spamming and the threat that poses to e-Commerce and Banking and tools that help and Enterprise toward of such threats. To quote Confucius, "As the water shapes itself to the vessel that contains it, so does a wise man adapts himself to circumstances." And the journey of evolution and progression will continue and institutions and enterprises will continue to become smarter and more and more technology savvy. Enterprises and businesses across all genre and spectrum are trying their level best to adopt to change and move on with the changing requirements of technology and as enterprises and companies upgrade and speed up their digital transformations and move their outdate heirloom systems to the cloud, archaic partners that don't keep up will be left behind. Note: T&F does not sell or distribute the Hardback in India, Pakistan, Nepal, Bhutan, Bangladesh and Sri Lanka.

This book describes capacity building in strategic and non-strategic machine tool technology. It includes machine building in sectors such as machine tools, automobiles, home appliances, energy, and biomedical engineering, along with case studies. The book offers guidelines for capacity building in academia, covering how to promote enterprises of functional reverse engineering enterprises. It also discusses machine tool development, engineering design, prototyping of strategic, and non-strategies machine tools, as well as presenting communication strategies and IoT, along with case studies. Professionals from the CNC (Computer Numeric Control) machine tools industry, industrial and manufacturing engineers, and students and faculty in engineering disciplines will find interest in this book.

Recommender systems use information filtering to predict user preferences. They are becoming a vital part of e-business and are used in a wide variety of industries, ranging from entertainment and social networking to information technology, tourism, education, agriculture, healthcare, manufacturing, and retail. Recommender Systems: Algorithms and Applications dives into the theoretical underpinnings of these systems and looks at how this theory is applied and implemented in actual systems. The book examines several classes of recommendation algorithms, including Machine learning algorithms Community detection algorithms Filtering algorithms Various efficient and robust product recommender systems using machine learning algorithms are helpful in filtering and exploring unseen data by users for better prediction and extrapolation of decisions. These are providing a wider range of solutions to such challenges as imbalanced data set problems, cold-start problems, and long tail problems. This book also looks at fundamental ontological positions that form the foundations of recommender systems and explain why certain recommendations are predicted over others. Techniques and approaches for developing recommender systems are also investigated. These can help with implementing algorithms as systems and include A latent-factor technique for model-based filtering systems Collaborative filtering approaches Content-based approaches Finally, this book examines actual systems for social networking, recommending consumer products, and predicting risk in software engineering projects.

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.

Program management in a technical environment is as much art as it is science. Effective program managers are able to combine management and leadership skills for the good of the program and the people entrusted to them. This book illuminates the entire life cycle of the program-from the customer's original concept to successful completion. It includes many helpful ideas and insights into why programs and program managers can fail. Much more importantly, it provides insights about how program managers can succeed. Program Management in Defense and High Tech Environments is organized as a chronological "tale" of a program life cycle, with "side trips" that cover the important concepts of leadership, claims and claims avoidance, earned value measurement (EVM), communication basics, negotiations, and coaching. The book begins with an overview of program management, discussing the role of program managers, their required skills and experience, and the types of programs and contracts. The remainder of the book provides more detail on the program manager's role and the environment in which he or she works. Understanding that academic explanations of program management activities can be dry, the author uses true-to-life stories to present the nuts and bolts of the work. These stories illustrate the science of program management and the art that is necessary for success. The book discusses many of the common program pitfalls. It explains how to detect and avoid scope creep-the unintended expansion of program scope. It details both internal and external scope creep and stresses the importance of constant vigilance to prevent cost overruns and schedule delays. Program Management in Defense and High Tech Environments is a comprehensive guide for early- and mid-career program managers to understand what they need to do to be successful. It is also a valuable resource for later-career program managers who want to learn through other program managers' successes and failures.

TensorFlow is a one-stop solution for building, monitoring, optimizing,and deploying your models. This practical guide to building deep learning models with the new features of TensorFlow 2.0is filled with engaging projects, simple language, and coverage of the latest algorithms. TensorFlow 2.0 in Action teaches you to use the new features of TensorFlow 2.0 to create advanced deep learning models. You'll learn by building hands-on projects including an image classifier that can recognize objects, a French-to-English machine translator, and even a neural network that can write fiction. You'll dive into the details of modern deep learning techniques including both transformer and attention models, and learn how pretrained models can solve your tricky data science- problems. TensorFlow is the go-to framework for putting deep learning into production. Created by Google, this ground breaking tool handles repetitive low-level operations and frees you up to focus on innovating your AIs.TensorFlow encompasses almost every element of a deep learning pipeline-aone-stop solution for building, monitoring, optimizing, and deploying your models.

Data-intensive systems are software applications that process and generate Big Data. Data-intensive systems support the use of large amounts of data strategically and efficiently to provide intelligence. For example, examining industrial sensor data or business process data can enhance production, guide proactive improvements of development processes, or optimize supply chain systems. Designing data-intensive software systems is difficult because distribution of knowledge across stakeholders creates a symmetry of ignorance, because a shared vision of the future requires the development of new knowledge that extends and synthesizes existing knowledge. Knowledge Management in the Development of Data-Intensive Systems addresses new challenges arising from knowledge management in the development of data-intensive software systems. These challenges concern requirements, architectural design, detailed design, implementation and maintenance. The book covers the current state and future directions of knowledge management in development of data-intensive software systems. The book features both academic and industrial contributions which discuss the role software engineering can play for addressing challenges that confront developing, maintaining and evolving systems;data-intensive software systems of cloud and mobile services; and the scalability requirements they imply. The book features software engineering approaches that can efficiently deal with data-intensive systems as well as applications and use cases benefiting from data-intensive systems. Providing a comprehensive reference on the notion of data-intensive systems from a technical and non-technical perspective, the book focuses uniquely on software engineering and knowledge management in the design and maintenance of data-intensive systems. The book covers constructing, deploying, and maintaining high quality software products and software engineering in and for dynamic and flexible environments. This book provides a holistic guide for those who need to understand the impact of variability on all aspects of the software life cycle. It leverages practical experience and evidence to look ahead at the challenges faced by organizations in a fast-moving world with increasingly fast-changing customer requirements and expectations.

Learn data science with Python by building five real-world projects! In Data Science Bookcamp you'll test and build your knowledge of Python and learn to handle the kind of open-ended problems that professional data scientists work on daily. Downloadable data sets and thoroughly-explained solutions help you lock in what you've learned, building your confidence and making you ready for an exciting new data science career. about the technologyIn real-world practice, data scientists create innovative solutions to novel open ended problems. Easy to learn and use, the Python language has become the de facto language for data science amongst researchers, developers, and business users. But knowing a few basic algorithms is not enough to tackle a vague and thorny problem. It takes relentless practice at cracking difficult data tasks to achieve mastery in the field. That's just what this book delivers. about the book Data Science Bookcamp is a comprehensive set of challenging projects carefully designed to grow your data science skills from novice to master. Veteran data scientist Leonard Apeltsin sets five increasingly difficult exercises that test your abilities against the kind of problems you'd encounter in the real world. As you solve each challenge, you'll acquire and expand the data science and Python skills you'll use as a professional data scientist. Ranging from text processing to machine learning, each project comes complete with a unique downloadable data set and a fully-explained step-by-step solution. Because these projects come from Dr. Apeltsin's vast experience, each solution highlights the most likely failure points along with practical advice for getting past unexpected pitfalls. When you wrap up these five awesome exercises, you'll have a diverse relevant skill set that's transferable to working in industry. what's inside Five in-depth Python exercises with full downloadable data sets Web scraping for text and images Organise datasets with clustering algorithms Visualize complex multi-variable datasets Train a decision tree machine learning algorithm about the readerFor readers who know the basics of Python. No prior data science or machine learning skills required. about the author Leonard Apeltsin is a senior data scientist and engineering lead at Primer AI, a startup that specializes in using advanced Natural Language Processing techniques to extract insight from terabytes of unstructured text data. His PhD research focused on bioinformatics that required analyzing millions of sequenced DNA patterns to uncover genetic links in deadly diseases.

The field of rock mechanics and rock engineering utilizes the basic laws of continuum mechanics and the techniques developed in computational mechanics. This book describes the basic concepts behind these fundamental laws and their utilization in practice irrespective of whether rock/rock mass contains discontinuities. This book consists of nine chapters and six appendices. The first four chapters are concerned with continuum mechanics aspects, which include the basic operations, definition of stress and strain tensors, and derivation of four fundamental conservation laws in the simplest yet precise manner. The next two chapters are the preparation for computational mechanics, which require constitutive laws of geomaterials relevant to each conservation law and the procedures for how to determine required parameters of the constitutive laws. Computational mechanics solves the resulting ordinary and partial differential equations. In Chapter 7, the methods of exact (closed-form) solutions are explained and they are applied to ordinary/partial differential equations with solvable boundary and initial conditions. In Chapter 8, the fundamentals of approximate solution methods are explained for one dimension first and then how to extend them to multi-dimensional problems. The readers are expected to learn and clearly understand how they are derived and applied to various problems in geomechanics. The final chapter involves the applications of the approximate methods to the actual problems in practice for geomechanical engineers, which cover the continuum to discontinuum, including the stress state of the earth as well as the ground motions induced by earthquakes. Six appendices are provided to have a clear understanding of continuum mechanics operations and procedures for how to deal with discontinuities/interfaces often encountered in rock mechanics and rock engineering.

This book provides the latest research advances in the field of system reliability assurance and engineering. It contains reference material for applications of reliability in system engineering, offering a theoretical sound background with adequate numerical illustrations. Included are concepts pertaining to reliability analysis, assurance techniques and methodologies, tools, and practical applications of system reliability modeling and allocation. The collection discusses various soft computing techniques like artificial intelligence and particle swarm optimization approach for reliability assessment. Importance of differentiating between the optimal release time and testing stop time of the software has been explicitly discussed and presented in the book. Features: Creates understanding of the costs associated with complex systems Covers reliability measurement of engineering systems Incorporates an efficient effort-based expenditure policy incorporating cost and reliability criteria Provides information for optimal testing stop and release time of software system Presents software performance and security layout Addresses reliability prediction and its maintenance through advanced analytics techniques Overall, System Reliability Management: Solutions and Techniques is a collaborative and interdisciplinary approach for better communication of problems and solutions to increase the performance of the system for better utilization and resource management.

This title includes a number of Open Access chapters. Data structure and software engineering is an integral part of computer science. This volume presents new approaches and methods to knowledge sharing, brain mapping, data integration, and data storage. The author describes how to manage an organization's business process and domain data and presents new software and hardware testing methods. The book introduces a game development framework used as a learning aid in a software engineering at the university level. It also features a review of social software engineering metrics and methods for processing business information. It explains how to use Pegasys to create and manage sequence analysis workflows.