Sharing Data and Models in Software Engineering PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Sharing Data and Models in Software Engineering PDF full book. Access full book title Sharing Data and Models in Software Engineering by Tim Menzies. Download full books in PDF and EPUB format.
Author: Tim Menzies
Publisher: Morgan Kaufmann
ISBN: 0124173071
Category : Computers
Languages : en
Pages : 415
Get Book Here
Book Description
Data Science for Software Engineering: Sharing Data and Models presents guidance and procedures for reusing data and models between projects to produce results that are useful and relevant. Starting with a background section of practical lessons and warnings for beginner data scientists for software engineering, this edited volume proceeds to identify critical questions of contemporary software engineering related to data and models. Learn how to adapt data from other organizations to local problems, mine privatized data, prune spurious information, simplify complex results, how to update models for new platforms, and more. Chapters share largely applicable experimental results discussed with the blend of practitioner focused domain expertise, with commentary that highlights the methods that are most useful, and applicable to the widest range of projects. Each chapter is written by a prominent expert and offers a state-of-the-art solution to an identified problem facing data scientists in software engineering. Throughout, the editors share best practices collected from their experience training software engineering students and practitioners to master data science, and highlight the methods that are most useful, and applicable to the widest range of projects. - Shares the specific experience of leading researchers and techniques developed to handle data problems in the realm of software engineering - Explains how to start a project of data science for software engineering as well as how to identify and avoid likely pitfalls - Provides a wide range of useful qualitative and quantitative principles ranging from very simple to cutting edge research - Addresses current challenges with software engineering data such as lack of local data, access issues due to data privacy, increasing data quality via cleaning of spurious chunks in data
Author: Tim Menzies
Publisher: Morgan Kaufmann
ISBN: 0124173071
Category : Computers
Languages : en
Pages : 415
Get Book Here
Book Description
Data Science for Software Engineering: Sharing Data and Models presents guidance and procedures for reusing data and models between projects to produce results that are useful and relevant. Starting with a background section of practical lessons and warnings for beginner data scientists for software engineering, this edited volume proceeds to identify critical questions of contemporary software engineering related to data and models. Learn how to adapt data from other organizations to local problems, mine privatized data, prune spurious information, simplify complex results, how to update models for new platforms, and more. Chapters share largely applicable experimental results discussed with the blend of practitioner focused domain expertise, with commentary that highlights the methods that are most useful, and applicable to the widest range of projects. Each chapter is written by a prominent expert and offers a state-of-the-art solution to an identified problem facing data scientists in software engineering. Throughout, the editors share best practices collected from their experience training software engineering students and practitioners to master data science, and highlight the methods that are most useful, and applicable to the widest range of projects. - Shares the specific experience of leading researchers and techniques developed to handle data problems in the realm of software engineering - Explains how to start a project of data science for software engineering as well as how to identify and avoid likely pitfalls - Provides a wide range of useful qualitative and quantitative principles ranging from very simple to cutting edge research - Addresses current challenges with software engineering data such as lack of local data, access issues due to data privacy, increasing data quality via cleaning of spurious chunks in data
Author: Steve Tockey
Publisher: John Wiley & Sons
ISBN: 1119546672
Category : Computers
Languages : en
Pages : 1188
Get Book Here
Book Description
A guide to the application of the theory and practice of computing to develop and maintain software that economically solves real-world problem How to Engineer Software is a practical, how-to guide that explores the concepts and techniques of model-based software engineering using the Unified Modeling Language. The author—a noted expert on the topic—demonstrates how software can be developed and maintained under a true engineering discipline. He describes the relevant software engineering practices that are grounded in Computer Science and Discrete Mathematics. Model-based software engineering uses semantic modeling to reveal as many precise requirements as possible. This approach separates business complexities from technology complexities, and gives developers the most freedom in finding optimal designs and code. The book promotes development scalability through domain partitioning and subdomain partitioning. It also explores software documentation that specifically and intentionally adds value for development and maintenance. This important book: Contains many illustrative examples of model-based software engineering, from semantic model all the way to executable code Explains how to derive verification (acceptance) test cases from a semantic model Describes project estimation, along with alternative software development and maintenance processes Shows how to develop and maintain cost-effective software that solves real-world problems Written for graduate and undergraduate students in software engineering and professionals in the field, How to Engineer Software offers an introduction to applying the theory of computing with practice and judgment in order to economically develop and maintain software.
Author: Michael Felderer
Publisher: Springer Nature
ISBN: 3030324893
Category : Computers
Languages : en
Pages : 520
Get Book Here
Book Description
This book presents contemporary empirical methods in software engineering related to the plurality of research methodologies, human factors, data collection and processing, aggregation and synthesis of evidence, and impact of software engineering research. The individual chapters discuss methods that impact the current evolution of empirical software engineering and form the backbone of future research. Following an introductory chapter that outlines the background of and developments in empirical software engineering over the last 50 years and provides an overview of the subsequent contributions, the remainder of the book is divided into four parts: Study Strategies (including e.g. guidelines for surveys or design science); Data Collection, Production, and Analysis (highlighting approaches from e.g. data science, biometric measurement, and simulation-based studies); Knowledge Acquisition and Aggregation (highlighting literature research, threats to validity, and evidence aggregation); and Knowledge Transfer (discussing open science and knowledge transfer with industry). Empirical methods like experimentation have become a powerful means of advancing the field of software engineering by providing scientific evidence on software development, operation, and maintenance, but also by supporting practitioners in their decision-making and learning processes. Thus the book is equally suitable for academics aiming to expand the field and for industrial researchers and practitioners looking for novel ways to check the validity of their assumptions and experiences. Chapter 17 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Author: Hassan Gomaa
Publisher: Cambridge University Press
ISBN: 1139494732
Category : Computers
Languages : en
Pages : 577
Get Book Here
Book Description
This book covers all you need to know to model and design software applications from use cases to software architectures in UML and shows how to apply the COMET UML-based modeling and design method to real-world problems. The author describes architectural patterns for various architectures, such as broker, discovery, and transaction patterns for service-oriented architectures, and addresses software quality attributes including maintainability, modifiability, testability, traceability, scalability, reusability, performance, availability, and security. Complete case studies illustrate design issues for different software architectures: a banking system for client/server architecture, an online shopping system for service-oriented architecture, an emergency monitoring system for component-based software architecture, and an automated guided vehicle for real-time software architecture. Organized as an introduction followed by several short, self-contained chapters, the book is perfect for senior undergraduate or graduate courses in software engineering and design, and for experienced software engineers wanting a quick reference at each stage of the analysis, design, and development of large-scale software systems.
Author: Stephen H. Kan
Publisher: Addison-Wesley Professional
ISBN: 9780201729153
Category : Computers
Languages : en
Pages : 564
Get Book Here
Book Description
""This is the single best book on software quality engineering and metrics that I've encountered."" --Capers Jones, from the Foreword"Metrics and Models in Software Quality Engineering, Second Edition," is the definitive book on this essential topic of software development. Comprehensive in scope with extensive industry examples, it shows how to measure software quality and use measurements to improve the software development process. Four major categories of quality metrics and models are addressed: quality management, software reliability and projection, complexity, and customer view. In addition, the book discusses the fundamentals of measurement theory, specific quality metrics and tools, and methods for applying metrics to the software development process.New chapters bring coverage of critical topics, including: In-process metrics for software testingMetrics for object-oriented software developmentAvailability metricsMethods for conducting in-process quality assessments and software project assessmentsDos and Don'ts of Software Process Improvement, by Patrick O'TooleUsing Function Point Metrics to Measure Software Process Improvement, by Capers Jones In addition to the excellent balance of theory, techniques, and examples, this book is highly instructive and practical, covering one of the most important topics in software development--quality engineering. 0201729156B08282002
Author: Christian Bird
Publisher: Elsevier
ISBN: 0124115438
Category : Computers
Languages : en
Pages : 673
Get Book Here
Book Description
The Art and Science of Analyzing Software Data provides valuable information on analysis techniques often used to derive insight from software data. This book shares best practices in the field generated by leading data scientists, collected from their experience training software engineering students and practitioners to master data science. The book covers topics such as the analysis of security data, code reviews, app stores, log files, and user telemetry, among others. It covers a wide variety of techniques such as co-change analysis, text analysis, topic analysis, and concept analysis, as well as advanced topics such as release planning and generation of source code comments. It includes stories from the trenches from expert data scientists illustrating how to apply data analysis in industry and open source, present results to stakeholders, and drive decisions. - Presents best practices, hints, and tips to analyze data and apply tools in data science projects - Presents research methods and case studies that have emerged over the past few years to further understanding of software data - Shares stories from the trenches of successful data science initiatives in industry
Author: Jiayao Wang
Publisher: Springer Nature
ISBN: 9811606145
Category : Science
Languages : en
Pages : 646
Get Book Here
Book Description
This book reviews and summarizes the development and achievement in cartography and geographic information engineering in China over the past 60 years after the founding of the People's Republic of China. It comprehensively reflects cartography, as a traditional discipline, has almost the same long history with the world's first culture and has experienced extraordinary and great changes. The book consists of nineteen thematic chapters. Each chapter is in accordance with the unified directory structure, introduction, development process, major study achievements, problem and prospect, representative works, as well as a lot of references. It is useful as a reference both for scientists and technicians who are engaged in teaching, researching and engineering of cartography and geographic information engineering.
Author: Gudni Gudnason
Publisher: CRC Press
ISBN: 0415621283
Category : Technology & Engineering
Languages : en
Pages : 978
Get Book Here
Book Description
Since 1994, the European Conferences of Product and Process Modelling (www.ecppm.org) have provided a review of research, development and industrial implementation of product and process model technology in the Architecture, Engineering, Construction and Facilities Management (AEC/FM) industry. Product/Building Information Modelling has matured significantly in the last few years and has never been closer to having a permanent impact on the AEC/FM industry as a mainstream technology. In this context the 9th European Conference of Product and Process Modelling provided a forum for leading experts to discuss the latest achievements, emerging trends and future directions in product and process modelling technology in this dynamic and fragmented industry, focusing on integrated project working, value-based life cycle management and intelligent and sustainable buildings and construction. eWork and eBusiness in Architecture, Engineering and Construction 2012 provides a comprehensive overview of topics including BIM in all life-cycle stages, ICT for energy efficiency, smart buildings and environmental performance, energy and building simulation, knowledge and semantic modelling, visualization technologies as well as tools and methods to support innovations in design and construction processes. It further includes the proceedings of the 3rd Workshop on eeBuildings Data Models (Energy Efficiency Vocabularies), which aim to identify ICT Energy Efficiency Vocabularies and Ontologies to foster interoperability of Energy Efficiency Management Systems. eWork and eBusiness in Architecture, Engineering and Construction 2012 will be of interest to academics and professionals working in the interdisciplinary area of information technology in architecture, engineering and construction.
Author: Jeff Garland
Publisher: John Wiley & Sons
ISBN: 0470856386
Category : Computers
Languages : en
Pages : 278
Get Book Here
Book Description
The purpose of large-scale software architecture is to capture and describe practical representations to make development teams more effective. In this book the authors show how to utilise software architecture as a tool to guide the development instead of capturing the architectural details after all the design decisions have been made. * Offers a concise description of UML usage for large-scale architecture * Discusses software architecture and design principles * Technology and vendor independent
Author: Werner Liebregts
Publisher: Springer Nature
ISBN: 303119554X
Category : Business & Economics
Languages : en
Pages : 532
Get Book Here
Book Description
The fast-paced technological development and the plethora of data create numerous opportunities waiting to be exploited by entrepreneurs. This book provides a detailed, yet practical, introduction to the fundamental principles of data science and how entrepreneurs and would-be entrepreneurs can take advantage of it. It walks the reader through sections on data engineering, and data analytics as well as sections on data entrepreneurship and data use in relation to society. The book also offers ways to close the research and practice gaps between data science and entrepreneurship. By having read this book, students of entrepreneurship courses will be better able to commercialize data-driven ideas that may be solutions to real-life problems. Chapters contain detailed examples and cases for a better understanding. Discussion points or questions at the end of each chapter help to deeply reflect on the learning material.
