Artificial Intelligence Methods For Software Engineering
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Artificial Intelligence Methods For Software Engineering
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Author : Meir Kalech
language : en
Publisher: World Scientific
Release Date : 2021-06-15
Artificial Intelligence Methods For Software Engineering written by Meir Kalech and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-15 with Computers categories.
Software is an integral part of our lives today. Modern software systems are highly complex and often pose new challenges in different aspects of Software Engineering (SE).Artificial Intelligence (AI) is a growing field in computer science that has been proven effective in applying and developing AI techniques to address various SE challenges.This unique compendium covers applications of state-of-the-art AI techniques to the key areas of SE (design, development, debugging, testing, etc).All the materials presented are up-to-date. This reference text will benefit researchers, academics, professionals, and postgraduate students in AI, machine learning and software engineering.Related Link(s)
Artificial Intelligence Methods In Software Testing
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Author : Mark Last
language : en
Publisher: World Scientific
Release Date : 2004-06-03
Artificial Intelligence Methods In Software Testing written by Mark Last and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-06-03 with Computers categories.
An inadequate infrastructure for software testing is causing major losses to the world economy. The characteristics of software quality problems are quite similar to other tasks successfully tackled by artificial intelligence techniques. The aims of this book are to present state-of-the-art applications of artificial intelligence and data mining methods to quality assurance of complex software systems, and to encourage further research in this important and challenging area.
Advances In Machine Learning Applications In Software Engineering
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Author : Du Zhang
language : en
Publisher: IGI Global
Release Date : 2007
Advances In Machine Learning Applications In Software Engineering written by Du Zhang and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Computers categories.
Machine learning is the study of building computer programs that improve their performance through experience. To meet the challenge of developing and maintaining larger and complex software systems in a dynamic and changing environment, machine learning methods have been playing an increasingly important role in many software development and maintenance tasks. Advances in Machine Learning Applications in Software Engineering provides analysis, characterization and refinement of software engineering data in terms of machine learning methods. This book depicts applications of several machine learning approaches in software systems development and deployment, and the use of machine learning methods to establish predictive models for software quality. Advances in Machine Learning Applications in Software Engineering offers readers suggestions by proposing future work and areas in this emerging research field.
Artificial Intelligence Methods For Optimization Of The Software Testing Process
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Author : Sahar Tahvili
language : en
Publisher: Elsevier
Release Date : 2022-07-26
Artificial Intelligence Methods For Optimization Of The Software Testing Process written by Sahar Tahvili and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-26 with Computers categories.
Artificial Intelligence Methods for Optimization of the Software Testing Process: With Practical Examples and Exercises presents different AI-based solutions for overcoming the uncertainty found in many initial testing problems. The concept of intelligent decision making is presented as a multi-criteria, multi-objective undertaking. The book provides guidelines on how to manage diverse types of uncertainty with intelligent decision-making that can help subject matter experts in many industries improve various processes in a more efficient way. As the number of required test cases for testing a product can be large (in industry more than 10,000 test cases are usually created). Executing all these test cases without any particular order can impact the results of the test execution, hence this book fills the need for a comprehensive resource on the topics on the how's, what's and whys. To learn more about Elsevier's Series, Uncertainty, Computational Techniques and Decision Intelligence, please visit this link: https://www.elsevier.com/books-and-journals/book-series/uncertainty-computational-techniques-and-decision-intelligence Presents one of the first empirical studies in the field, contrasting theoretical assumptions on innovations in a real industrial environment with a large set of use cases from developed and developing testing processes at various large industries Explores specific comparative methodologies, focusing on developed and developing AI-based solutions Serves as a guideline for conducting industrial research in the artificial intelligence and software testing domain Explains all proposed solutions through real industrial case studies
Computational Intelligence In Software Engineering
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Author : Witold Pedrycz
language : en
Publisher: World Scientific
Release Date : 1998
Computational Intelligence In Software Engineering written by Witold Pedrycz and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with Computers categories.
This unique volume is the first publication on software engineering and computational intelligence (CI) viewed as a synergistic interplay of neurocomputing, granular computation (including fuzzy sets and rough sets), and evolutionary methods. It presents a unified view of CI in the context of software engineering. The book addresses a number of crucial issues: what is CI, what role does it play in software development, how are CI elements built into successive phases of the software life cycle, and what is the role played by CI in quantifying fundamental features of software artifacts? With contributions from leading researchers and practitioners, the book provides the reader with a wealth of new concepts and approaches, complete algorithms, in-depth case studies, and thought-provoking exercises. The topics coverage include neurocomputing, granular as well as evolutionary computing, object-oriented analysis and design in software engineering. There is also an extensive bibliography.
Artificial Intelligence Methods In Software Testing
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Author : Mark Last
language : en
Publisher: World Scientific
Release Date : 2004
Artificial Intelligence Methods In Software Testing written by Mark Last and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Computers categories.
- Coverage of novel methods for software testing and software quality assurance - Introduction to state-of-the-art data mining models and techniques - Analyses of new and promising application domains of artificial intelligence and data mining in software quality engineering - Contributions from leading authors in the fields of software engineering and data mining.
Deep Learning For Coders With Fastai And Pytorch
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Author : Jeremy Howard
language : en
Publisher: O'Reilly Media
Release Date : 2020-06-29
Deep Learning For Coders With Fastai And Pytorch written by Jeremy Howard and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-29 with Computers categories.
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Artificial Intelligence Applications For Improved Software Engineering Development
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Author : Farid Meziane
language : en
Publisher: Business Science Reference
Release Date : 2010
Artificial Intelligence Applications For Improved Software Engineering Development written by Farid Meziane and has been published by Business Science Reference this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Computers categories.
"This book provides an overview of useful techniques in artificial intelligence for future software development along with critical assessment for further advancement"--Provided by publisher.
Machine Learning Applications In Software Engineering
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Author : Du Zhang
language : en
Publisher: World Scientific
Release Date : 2005-02-21
Machine Learning Applications In Software Engineering written by Du Zhang and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-02-21 with Computers categories.
Machine learning deals with the issue of how to build computer programs that improve their performance at some tasks through experience. Machine learning algorithms have proven to be of great practical value in a variety of application domains. Not surprisingly, the field of software engineering turns out to be a fertile ground where many software development and maintenance tasks could be formulated as learning problems and approached in terms of learning algorithms. This book deals with the subject of machine learning applications in software engineering. It provides an overview of machine learning, summarizes the state-of-the-practice in this niche area, gives a classification of the existing work, and offers some application guidelines. Also included in the book is a collection of previously published papers in this research area.