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130626 ||| eng |
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|a 9781441973269
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|a Xiong, Jay
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|a New Software Engineering Paradigm Based on Complexity Science
|h Elektronische Ressource
|b An Introduction to NSE
|c by Jay Xiong
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| 250 |
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|a 1st ed. 2011
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| 260 |
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|a New York, NY
|b Springer New York
|c 2011, 2011
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| 300 |
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|a XXXV, 746 p
|b online resource
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|a Introduction -- The Process models of the old-established software engineering paradigm -- The critical problems existing with the old-established software engineering paradigm -- The foundation for establishing NSE: complexity science -- Prediction and practices : a new round of industrial revolution driven by complexity science, and a general paradigm-shift framework proposed -- Outline of NSE paradigm -- NSE process model -- The facility for automated and bidirectional traceability -- NSE software development methodology driven by defect-prevention and traceability -- NSE software diagramming paradigm based on interactive and traceable 3J-Graphics proposed -- The basis of software testing -- Software test case design -- Various kinds of software testing -- NSE software testing paradigm based on Transparent-box testing approach proposed -- NSE software quality assurance paradigm driven by defect prevention -- NSE software maintenance paradigm: systematic, disciplined, and quantifiable -- NSE documentation paradigm: traceable and consistent with the source code -- NSE project management paradigm: seamlessly combined with the project development process -- Technologies and algorithms innovated for supporting NSE -- NSE support tools and NSE support platform, Panorama++ and SilverBullet -- NSE applications -- Candidate of "Silver Bullet"
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| 653 |
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|a Complexity
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| 653 |
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|a Algorithms
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| 653 |
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|a Applications of Graph Theory and Complex Networks
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| 653 |
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|a Computational complexity
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| 653 |
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|a Physics
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| 653 |
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|a Algorithm Analysis and Problem Complexity
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| 041 |
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|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 856 |
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|u https://doi.org/10.1007/978-1-4419-7326-9?nosfx=y
|x Verlag
|3 Volltext
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|a 620
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| 520 |
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|a In New Software Engineering Paradigm Based on Complexity Science: An Introduction to NSE, Xiong details how complexity science can be a powerful means to solve all critical issues existing in today’s software engineering through a two-step process. Step one discusses shifting the engineering paradigm to one based on nonlinear process and complexity science. For that, he posits the Nonlinear Software Engineering (NSE) paradigm. And step two applies complexity science to efficiently handle the problems of an individual complex system. This text introduces many new concepts, ideas, algorithms, models, methods, techniques and tools. It is aimed at anyone who has any relation to computer science and software engineering.
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| 520 |
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|a The tools NSE_CLICK- an automatic acceptance testing platform for outsourcing (or internally developed) C/C++ products, and NSE_CLICK_J - an automatic acceptance testing platform for outsourcing (or internally developed) Java products are particularly designed for non-technical readers to view/review how the acceptance testing of a software product developed with NSE can be performed automatically, and how the product developed with NSE is truly maintainable at the customer site
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|a Title: New Software Engineering Paradigm Based on Complexity Science: An Introduction to NSE Author: Jay Xiong Software has become the driving force for the development of all kinds of businesses, engineering, sciences and the global economy. But software itself is not well engineered, resulting in a total economic cost that is disturbingly high. Jay Xiong, a leading entrepreneur in the field of software engineering, realized many of the problems in software engineering stem from an incorrect paradigm. Software is a nonlinear complex system, but the existing software engineering paradigm is based on linear thinking, reductionism, and a superposition principle that the whole of a system is the sum of its parts. Xiong believes that the solution to software problems lies with complexity science, which is the science of studying complex systems with many interactive components.
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