Machine Learning Applications In Software Engineering


Machine Learning Applications In Software Engineering
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Machine Learning Applications In Software Engineering


Machine Learning Applications In Software Engineering
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Author : Du Zhang
language : en
Publisher: World Scientific
Release Date : 2005

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 with Computers categories.


A collection of previously published articles from a variety of publications.



Advances In Machine Learning Applications In Software Engineering


Advances In Machine Learning Applications In Software Engineering
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Author : Zhang, Du
language : en
Publisher: IGI Global
Release Date : 2006-10-31

Advances In Machine Learning Applications In Software Engineering written by Zhang, Du and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-10-31 with Computers categories.


"This book provides analysis, characterization and refinement of software engineering data in terms of machine learning methods. It 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 while offering readers suggestions by proposing future work in this emerging research field"--Provided by publisher.



Artificial Intelligence Methods For Software Engineering


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)



Machine Learning Infrastructure And Best Practices For Software Engineers


Machine Learning Infrastructure And Best Practices For Software Engineers
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Author : Miroslaw Staron
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-01-31

Machine Learning Infrastructure And Best Practices For Software Engineers written by Miroslaw Staron and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-31 with Computers categories.


Efficiently transform your initial designs into big systems by learning the foundations of infrastructure, algorithms, and ethical considerations for modern software products Key Features Learn how to scale-up your machine learning software to a professional level Secure the quality of your machine learning pipeline at runtime Apply your knowledge to natural languages, programming languages, and images Book DescriptionAlthough creating a machine learning pipeline or developing a working prototype of a software system from that pipeline is easy and straightforward nowadays, the journey toward a professional software system is still extensive. This book will help you get to grips with various best practices and recipes that will help software engineers transform prototype pipelines into complete software products. The book begins by introducing the main concepts of professional software systems that leverage machine learning at their core. As you progress, you’ll explore the differences between traditional, non-ML software, and machine learning software. The initial best practices will guide you in determining the type of software you need for your product. Subsequently, you will delve into algorithms, covering their selection, development, and testing before exploring the intricacies of the infrastructure for machine learning systems by defining best practices for identifying the right data source and ensuring its quality. Towards the end, you’ll address the most challenging aspect of large-scale machine learning systems – ethics. By exploring and defining best practices for assessing ethical risks and strategies for mitigation, you will conclude the book where it all began – large-scale machine learning software.What you will learn Identify what the machine learning software best suits your needs Work with scalable machine learning pipelines Scale up pipelines from prototypes to fully fledged software Choose suitable data sources and processing methods for your product Differentiate raw data from complex processing, noting their advantages Track and mitigate important ethical risks in machine learning software Work with testing and validation for machine learning systems Who this book is for If you’re a machine learning engineer, this book will help you design more robust software, and understand which scaling-up challenges you need to address and why. Software engineers will benefit from best practices that will make your products robust, reliable, and innovative. Decision makers will also find lots of useful information in this book, including guidance on what to look for in a well-designed machine learning software product.



Computational Intelligence Applications For Software Engineering Problems


Computational Intelligence Applications For Software Engineering Problems
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Author : Parma Nand
language : en
Publisher: CRC Press
Release Date : 2023-02-10

Computational Intelligence Applications For Software Engineering Problems written by Parma Nand and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-10 with Computers categories.


This new volume explores the computational intelligence techniques necessary to carry out different software engineering tasks. Software undergoes various stages before deployment, such as requirements elicitation, software designing, software project planning, software coding, and software testing and maintenance. Every stage is bundled with a number of tasks or activities to be performed. Due to the large and complex nature of software, these tasks can become costly and error prone. This volume aims to help meet these challenges by presenting new research and practical applications in intelligent techniques in the field of software engineering. Computational Intelligence Applications for Software Engineering Problems discusses techniques and presents case studies to solve engineering challenges using machine learning, deep learning, fuzzy-logic-based computation, statistical modeling, invasive weed meta-heuristic algorithms, artificial intelligence, the DevOps model, time series forecasting models, and more.



Artificial Intelligence And Software Engineering


Artificial Intelligence And Software Engineering
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Author : Derek Partridge
language : en
Publisher: Routledge
Release Date : 2013-04-11

Artificial Intelligence And Software Engineering written by Derek Partridge and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-04-11 with Business & Economics categories.


Managers, business owners, computer literate individuals, software developers, students, and researchers--all are looking for an understanding of artificial intelligence (AI) and what might be in the future. In this literate yet easy-to-read discussion, Derek Partridge explains what artificial intelligence can and cannot do, and what it holds for applications such as banking, financial services, and expert systems of all kinds. Topics include: the strengths and weaknesses of software development and engineering; machine learning and its promises and problems; expert systems and success stories; and practical software through artificial intelligence.



Automated Software Engineering A Deep Learning Based Approach


Automated Software Engineering A Deep Learning Based Approach
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Author : Suresh Chandra Satapathy
language : en
Publisher: Springer Nature
Release Date : 2020-01-07

Automated Software Engineering A Deep Learning Based Approach written by Suresh Chandra Satapathy and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-07 with Technology & Engineering categories.


This book discusses various open issues in software engineering, such as the efficiency of automated testing techniques, predictions for cost estimation, data processing, and automatic code generation. Many traditional techniques are available for addressing these problems. But, with the rapid changes in software development, they often prove to be outdated or incapable of handling the software’s complexity. Hence, many previously used methods are proving insufficient to solve the problems now arising in software development. The book highlights a number of unique problems and effective solutions that reflect the state-of-the-art in software engineering. Deep learning is the latest computing technique, and is now gaining popularity in various fields of software engineering. This book explores new trends and experiments that have yielded promising solutions to current challenges in software engineering. As such, it offers a valuable reference guide for a broad audience including systems analysts, software engineers, researchers, graduate students and professors engaged in teaching software engineering.



The Pragmatic Programmer For Machine Learning


The Pragmatic Programmer For Machine Learning
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Author : Marco Scutari
language : en
Publisher: Chapman & Hall/CRC
Release Date : 2025-04

The Pragmatic Programmer For Machine Learning written by Marco Scutari and has been published by Chapman & Hall/CRC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04 with Machine learning categories.


Gives a holistic approach to machine learning and data science applications, from design to deployment and quality assurance, as an overarching cyclical process; Bridges machine learning and software engineering to build a shared set of best practices useful to both academia and the industry; Discusses deployment options for different types of models and data to help practitioners reason and make informed choices. Emphasizes the role of coding standards and software architecture alongside statistical rigor to implement reproducible and scalable machine learning models Key Features: A complete guide to software engineering for machine learning and data science applications, from choosing the right hardware to analysing algorithms and designing scalable architectures. Surveys the state of the art of the software and frameworks used to build and run machine learning applications, comparing and contrasting their trade-offs. Comes with a complete case study in natural language understanding which illustrates the principles and the tools covered in the book. Code available from GitHub. Provides a multi-disciplinary view of how traditional software learning practices can be integrated with the workflows of domain experts and the unique characteristics of software in which data play a central role.



Deep Learning Applications And Intelligent Decision Making In Engineering


Deep Learning Applications And Intelligent Decision Making In Engineering
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Author : Senthilnathan, Karthikrajan
language : en
Publisher: IGI Global
Release Date : 2020-10-23

Deep Learning Applications And Intelligent Decision Making In Engineering written by Senthilnathan, Karthikrajan and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-23 with Technology & Engineering categories.


Deep learning includes a subset of machine learning for processing the unsupervised data with artificial neural network functions. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. When applied to engineering, deep learning can have a great impact on the decision-making process. Deep Learning Applications and Intelligent Decision Making in Engineering is a pivotal reference source that provides practical applications of deep learning to improve decision-making methods and construct smart environments. Highlighting topics such as smart transportation, e-commerce, and cyber physical systems, this book is ideally designed for engineers, computer scientists, programmers, software engineers, research scholars, IT professionals, academicians, and postgraduate students seeking current research on the implementation of automation and deep learning in various engineering disciplines.



Big Data Analysis And Deep Learning Applications


Big Data Analysis And Deep Learning Applications
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Author : Thi Thi Zin
language : en
Publisher: Springer
Release Date : 2018-06-06

Big Data Analysis And Deep Learning Applications written by Thi Thi Zin and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-06 with Technology & Engineering categories.


This book presents a compilation of selected papers from the first International Conference on Big Data Analysis and Deep Learning Applications (ICBDL 2018), and focuses on novel techniques in the fields of big data analysis, machine learning, system monitoring, image processing, conventional neural networks, communication, industrial information, and their applications. Readers will find insights to help them realize more efficient algorithms and systems used in real-life applications and contexts, making the book an essential reference guide for academic researchers, professionals, software engineers in the industry, and regulators of aviation authorities.