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Author: Rudra Kumar Publisher: Partridge Publishing ISBN: 1543702414 Category : Computers Languages : en Pages : 57
Book Description
This book is a collection of taxonomy and review of contemporary model in the field of software development and maintenance. This book is basically the result of our passion toward the research of application of software engineering concepts. This work is derived from the need for accurate fault estimation in goals of quality programming and minimal maintenance overheads. State of art technologies have been discussed with respective experimental investigations and analysis. This work started out as a survey and then evolved according to our interest and proclivity into a work that emphasizes the aspects of software development. This book is intended to explain how the defect predictions are used to improve the quality of software development for easy analysis in a very simple way. It contains research that is useful to research scholars, engineers, and computing researchers.
Author: Rudra Kumar Publisher: Partridge Publishing ISBN: 1543702414 Category : Computers Languages : en Pages : 57
Book Description
This book is a collection of taxonomy and review of contemporary model in the field of software development and maintenance. This book is basically the result of our passion toward the research of application of software engineering concepts. This work is derived from the need for accurate fault estimation in goals of quality programming and minimal maintenance overheads. State of art technologies have been discussed with respective experimental investigations and analysis. This work started out as a survey and then evolved according to our interest and proclivity into a work that emphasizes the aspects of software development. This book is intended to explain how the defect predictions are used to improve the quality of software development for easy analysis in a very simple way. It contains research that is useful to research scholars, engineers, and computing researchers.
Author: Christian Bird Publisher: Elsevier ISBN: 0124115438 Category : Computers Languages : en Pages : 673
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: Ruchika Malhotra Publisher: CRC Press ISBN: 1498719732 Category : Computers Languages : en Pages : 486
Book Description
Empirical research has now become an essential component of software engineering yet software practitioners and researchers often lack an understanding of how the empirical procedures and practices are applied in the field. Empirical Research in Software Engineering: Concepts, Analysis, and Applications shows how to implement empirical research pro
Author: Xiao-Yuan Jing Publisher: Springer Nature ISBN: 9819928427 Category : Technology & Engineering Languages : en Pages : 210
Book Description
With the increasing complexity of and dependency on software, software products may suffer from low quality, high prices, be hard to maintain, etc. Software defects usually produce incorrect or unexpected results and behaviors. Accordingly, software defect prediction (SDP) is one of the most active research fields in software engineering and plays an important role in software quality assurance. Based on the results of SDP analyses, developers can subsequently conduct defect localization and repair on the basis of reasonable resource allocation, which helps to reduce their maintenance costs. This book offers a comprehensive picture of the current state of SDP research. More specifically, it introduces a range of machine-learning-based SDP approaches proposed for different scenarios (i.e., WPDP, CPDP, and HDP). In addition, the book shares in-depth insights into current SDP approaches’ performance and lessons learned for future SDP research efforts. We believe these theoretical analyses and emerging challenges will be of considerable interest to all researchers, graduate students, and practitioners who want to gain deeper insights into and/or find new research directions in SDP. It offers a comprehensive introduction to the current state of SDP and detailed descriptions of representative SDP approaches.
Author: Tim Menzies Publisher: Morgan Kaufmann ISBN: 0124173071 Category : Computers Languages : en Pages : 415
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: Pradeep Singh Publisher: John Wiley & Sons ISBN: 1119821886 Category : Computers Languages : en Pages : 480
Book Description
FUNDAMENTALS AND METHODS OF MACHINE AND DEEP LEARNING The book provides a practical approach by explaining the concepts of machine learning and deep learning algorithms, evaluation of methodology advances, and algorithm demonstrations with applications. Over the past two decades, the field of machine learning and its subfield deep learning have played a main role in software applications development. Also, in recent research studies, they are regarded as one of the disruptive technologies that will transform our future life, business, and the global economy. The recent explosion of digital data in a wide variety of domains, including science, engineering, Internet of Things, biomedical, healthcare, and many business sectors, has declared the era of big data, which cannot be analysed by classical statistics but by the more modern, robust machine learning and deep learning techniques. Since machine learning learns from data rather than by programming hard-coded decision rules, an attempt is being made to use machine learning to make computers that are able to solve problems like human experts in the field. The goal of this book is to present a??practical approach by explaining the concepts of machine learning and deep learning algorithms with applications. Supervised machine learning algorithms, ensemble machine learning algorithms, feature selection, deep learning techniques, and their applications are discussed. Also included in the eighteen chapters is unique information which provides a clear understanding of concepts by using algorithms and case studies illustrated with applications of machine learning and deep learning in different domains, including disease prediction, software defect prediction, online television analysis, medical image processing, etc. Each of the chapters briefly described below provides both a chosen approach and its implementation. Audience Researchers and engineers in artificial intelligence, computer scientists as well as software developers.
Author: Tim Menzies Publisher: Morgan Kaufmann ISBN: 0128042613 Category : Computers Languages : en Pages : 410
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: José Raúl Romero Publisher: Springer Nature ISBN: 9811999481 Category : Computers Languages : en Pages : 349
Book Description
This book offers a practical introduction to the use of artificial intelligence (AI) techniques to improve and optimise the various phases of the software development process, from the initial project planning to the latest deployment. All chapters were written by leading experts in the field and include practical and reproducible examples. Following the introductory chapter, Chapters 2-9 respectively apply AI techniques to the classic phases of the software development process: project management, requirement engineering, analysis and design, coding, cloud deployment, unit and system testing, and maintenance. Subsequently, Chapters 10 and 11 provide foundational tutorials on the AI techniques used in the preceding chapters: metaheuristics and machine learning. Given its scope and focus, the book represents a valuable resource for researchers, practitioners and students with a basic grasp of software engineering.
Author: IEEE Staff Publisher: ISBN: 9781728168524 Category : Languages : en Pages :
Book Description
THE 11th INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION AND NETWORKING TECHNOLOGIES (ICCCNT) aims to provide a forum that brings together International researchers from academia and practitioners in the industry to meet and exchange ideas and recent research work on all aspects of Information and Communication Technologies
Author: Matthew B. Dwyer Publisher: Springer ISBN: 3540712895 Category : Computers Languages : en Pages : 452
Book Description
This book constitutes the refereed proceedings of the 10th International Conference on Fundamental Approaches to Software Engineering, FASE 2007, held in Braga, Portugal in March/April 2007 as part of ETAPS 2007, the Joint European Conferences on Theory and Practice of Software. It covers evolution and agents, model driven development, tool demonstrations, distributed systems, specification, services, testing, analysis, and design.
Author: Rudra Kumar
Author: Rudra Kumar
Author: Christian Bird
Author: Ruchika Malhotra
Author: Xiao-Yuan Jing
Author: Tim Menzies
Author: Pradeep Singh
Author: Tim Menzies
Author: José Raúl Romero
Author: IEEE Staff
Author: Matthew B. Dwyer