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Author: Tim Menzies
Publisher: Morgan Kaufmann
ISBN: 0124173071
Category : Computers
Languages : en
Pages : 415
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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: Stephanie P. Bryant
Publisher: RTI Press
ISBN:
Category : Computers
Languages : en
Pages : 18
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Book Description
Increasingly, researchers are turning to computational models to understand the interplay of important variables on systems' behaviors. Although researchers may develop models that meet the needs of their investigation, application limitations—such as nonintuitive user interface features and data input specifications—may limit the sharing of these tools with other research groups. By removing these barriers, other research groups that perform related work can leverage these work products to expedite their own investigations. The use of software engineering practices can enable managed application production and shared research artifacts among multiple research groups by promoting consistent models, reducing redundant effort, encouraging rigorous peer review, and facilitating research collaborations that are supported by a common toolset. This report discusses three established software engineering practices—the iterative software development process, object-oriented methodology, and unified modeling language—and the applicability of these practices to computational model development. Our efforts to modify the MIDAS TranStat application to make it more user-friendly are presented as an example of how computational models that are based on research and developed using software engineering practices can benefit a broader audience of researchers.
Author: Steve Tockey
Publisher: John Wiley & Sons
ISBN: 1119546672
Category : Computers
Languages : en
Pages : 1147
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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: C. W. Loftus
Publisher: Prentice Hall PTR
ISBN:
Category : Computers
Languages : en
Pages : 288
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Book Description
Distributed Software Engineering provides practical suggestions, guidelines and rules for meeting the increasingly important requirement of developing software by the collaboration of independent organisations, partly independent organisations and teleworkers. A key theme is the controlled sharing of project information, which may be geographically distributed across diverse networks and computing environments. Distributed Software Engineering explores distributed, collaborative software engineering using experiments and case studies.
Author: John Ousterhout
Publisher: Yaknyam Publishing
ISBN: 9781732102200
Category :
Languages : en
Pages :
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Book Description
Author: Tim Menzies
Publisher: Morgan Kaufmann
ISBN: 0128042613
Category : Computers
Languages : en
Pages : 410
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Book Description
Perspectives on Data Science for Software Engineering presents the best practices of seasoned data miners in software engineering. The idea for this book was created during the 2014 conference at Dagstuhl, an invitation-only gathering of leading computer scientists who meet to identify and discuss cutting-edge informatics topics. At the 2014 conference, the concept of how to transfer the knowledge of experts from seasoned software engineers and data scientists to newcomers in the field highlighted many discussions. While there are many books covering data mining and software engineering basics, they present only the fundamentals and lack the perspective that comes from real-world experience. This book offers unique insights into the wisdom of the community’s leaders gathered to share hard-won lessons from the trenches. Ideas are presented in digestible chapters designed to be applicable across many domains. Topics included cover data collection, data sharing, data mining, and how to utilize these techniques in successful software projects. Newcomers to software engineering data science will learn the tips and tricks of the trade, while more experienced data scientists will benefit from war stories that show what traps to avoid. Presents the wisdom of community experts, derived from a summit on software analytics Provides contributed chapters that share discrete ideas and technique from the trenches Covers top areas of concern, including mining security and social data, data visualization, and cloud-based data Presented in clear chapters designed to be applicable across many domains
Author: Marco Brambilla
Publisher: Morgan & Claypool Publishers
ISBN: 1627056955
Category : Computers
Languages : en
Pages : 391
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Book Description
This book discusses how model-based approaches can improve the daily practice of software professionals. This is known as Model-Driven Software Engineering (MDSE) or, simply, Model-Driven Engineering (MDE). MDSE practices have proved to increase efficiency and effectiveness in software development, as demonstrated by various quantitative and qualitative studies. MDSE adoption in the software industry is foreseen to grow exponentially in the near future, e.g., due to the convergence of software development and business analysis. The aim of this book is to provide you with an agile and flexible tool to introduce you to the MDSE world, thus allowing you to quickly understand its basic principles and techniques and to choose the right set of MDSE instruments for your needs so that you can start to benefit from MDSE right away. The book is organized into two main parts. The first part discusses the foundations of MDSE in terms of basic concepts (i.e., models and transformations), driving principles, application scenarios, and current standards, like the well-known MDA initiative proposed by OMG (Object Management Group) as well as the practices on how to integrate MDSE in existing development processes. The second part deals with the technical aspects of MDSE, spanning from the basics on when and how to build a domain-specific modeling language, to the description of Model-to-Text and Model-to-Model transformations, and the tools that support the management of MDSE projects. The second edition of the book features: a set of completely new topics, including: full example of the creation of a new modeling language (IFML), discussion of modeling issues and approaches in specific domains, like business process modeling, user interaction modeling, and enterprise architecture complete revision of examples, figures, and text, for improving readability, understandability, and coherence better formulation of definitions, dependencies between concepts and ideas addition of a complete index of book content In addition to the contents of the book, more resources are provided on the book's website http://www.mdse-book.com, including the examples presented in the book.
Author: Erika Epstein
Publisher: Artech House
ISBN: 1608071391
Category : Architecture
Languages : en
Pages : 272
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Book Description
Building Information Modeling (BIM) is the process of generating and managing building data during a building's lifecycle. Today, more and more architectural firms have adopted BIM software and processes because it allows them to produce measurably more work of better quality, in shorter periods of time. Featuring case studies of firms of all sizes, this practical resource shows professionals how to implement BIM in the building industry around the globe. The book explains how BIM allows the data collected to plan, design and build projects to continue to be used and added to during the occupied life of the building. Readers also become knowledgeable about the changing role of architects within the building industry as they embed BIM in their workflow. From interoperability and open standards, knowledge sharing, and gathering data, to the BIM software suite, implementation planning, and project workflow, this authoritative volume provides a thorough understanding of key aspects of BIM that practitioners need to understand.
Author: Biren Prasad
Publisher: CRC Press
ISBN: 1000942171
Category : Technology & Engineering
Languages : en
Pages : 894
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Book Description
This book is a collection of papers presented at the 7th ISPE International Conference on Concurrent Engineering (CE): Research and Applications. The papers deal with different topics providing information on information modelling, CE in virtual environment, and standards in CE.
Author: Ashu Gupta, Abdalla Alameen
Publisher: Notion Press
ISBN: 1948146789
Category : Technology & Engineering
Languages : en
Pages : 130
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Book Description
Computer simulation modeling is a discipline gaining popularity in both the government and industry. It can assist in the design, creation and evaluation of complex systems. Designers, program managers, analysts and engineers use computer simulation modeling to understand and evaluate ‘what if’ case scenarios. One can model a real or proposed system using computer software, which is useful when changes to the actual system are difficult to implement, involve high costs or are impractical. Some examples of computer simulation modeling familiar to most of us include weather forecasting, flight simulators used for training pilots and car crash modeling. Modeling & Simulation (M&S) has become an important tool in all phases of the acquisition process and can be used within most systems’ lifecycles, including requirement analysis, architectural design, design and development, tests and verifications and operations and maintenance. The science of modeling and simulation strives to showcase the highest possible level of reality to determine the conditions necessary for optimal performance. Modeling and simulation is a multifaceted and complex field due to the numerous applications involved, particularly since M&S applications range from nuclear reactions to supermarket queuing.
