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Author: Rudra Kumar
Publisher: Partridge Publishing
ISBN: 1543702414
Category : Computers
Languages : en
Pages : 57
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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
Get Book Here
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: Xiao-Yuan Jing
Publisher: Springer Nature
ISBN: 9819928427
Category : Technology & Engineering
Languages : en
Pages : 210
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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: Christian Bird
Publisher: Elsevier
ISBN: 0124115438
Category : Computers
Languages : en
Pages : 673
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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
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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: Kai-Yuan Cai
Publisher: Springer Science & Business Media
ISBN: 1461555930
Category : Computers
Languages : en
Pages : 284
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Book Description
also in: THE KLUWER INTERNATIONAL SERIES ON ASIAN STUDIES IN COMPUTER AND INFORMATION SCIENCE, Volume 1
Author: Pradeep Singh
Publisher: John Wiley & Sons
ISBN: 1119821886
Category : Computers
Languages : en
Pages : 480
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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: Marco D'ambros
Publisher: Createspace Independent Publishing Platform
ISBN: 9781460953563
Category : Computers
Languages : en
Pages : 248
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Book Description
Software systems are subject to continuous changes to adapt to new and changing requirements. This phenomenon, known as software evolution, leads in the long term to software aging: The size and the complexity of systems increase, while their quality decreases. In this context, it is no wonder that software maintenance claims the most part of a software system's cost. The analysis of software evolution helps practitioners deal with the negative effects of software aging. With the advent of the Internet and the consequent widespread adoption of distributed development tools, such as software configuration management and issue tracking systems, a vast amount of valuable information concerning software evolution has become available. In the last two decades, researchers have focused on mining and analyzing this data, residing in various software repositories, to understand software evolution and support maintenance activities. However, most approaches target a specific maintenance task, and consider only one of the several facets of software evolution. Such approaches, and the infrastructures that implement them, cannot be extended to address different maintenance problems. In this dissertation, we propose an integrated view of software evolution that combines different evolutionary aspects. Our thesis is that an integrated and flexible approach supports an extensible set of software maintenance activities. To this aim, we present a meta-model that integrates two aspects of software evolution: source code and software defects. We implemented our approach in a framework that, by retrieving information from source code and defect repositories, serves as a basis to create analysis techniques and tools. To show the flexibility of our approach, we extended our meta-model and framework with e-mail information extracted from development mailing lists. To validate our thesis, we devised and evaluated, on top of our approach, a number of novel analysis techniques that achieve two goals: 1) Inferring the causes of problems in a software system. We propose three retrospective analysis techniques, based on interactive visualizations, to analyze the evolution of source code, software defects, and their co-evolution. These techniques support various maintenance tasks, such as system restructuring, re-documentation, and identification of critical software components. 2) Predicting the future of a software system. We present four analysis techniques aimed at anticipating the locations of future defects, and investigating the impact of certain source code properties on the presence of defects. They support two maintenance tasks: defect prediction and software quality analysis. By creating our framework and the mentioned techniques on top of it, we provide evidence that an integrated view of software evolution, combining source code and software defects information, supports an extensible set of software maintenance tasks.
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: 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: Wendy M. Bayer
Publisher:
ISBN:
Category : Computer software
Languages : en
Pages : 164
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Book Description
To be a top performer in its sector, an organization needs the right kind of information, on a regular basis, to make the right decisions. Organizations use information to become more efficient and to produce better-quality products. Measurement facilitates and accelerates organizational learning and supports corporate adaptation within the marketplace. Measurement provides a structure for learning from each project, whether or not it was a good experience. Measurement also helps an organization understand the gaps between how it is performing and the performance levels demanded by the constraints. In effect, measurement information becomes a competitive resource, and an effective measurement process becomes an organizational discriminator. A well-designed measurement system collects the data that is necessary for data mining. The data that is found at the operational level of an organization. It feeds the data analysis systems, typically information systems (IS), and data mining processes that allow organizations to gain knowledge about themselves, their business environment, products, etc. It is an effective tool for decision support, and it leads to business intelligence (BI). It is business intelligence and the use of information technology that give organizations a competitive advantage, by modeling outcomes and discovering patterns in data. This thesis aims to prove that predictive models such as Kandler's defect prediction model for software development contribute to the overall measurement process and business intelligence of an organization, an ultimately contribute to decision support and strategic planning.
