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Enhancing software fault prediction ...

Rashid, Ekbal, (1976-)

Enhancing software fault prediction with machine learningemerging research and opportunities /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Enhancing software fault prediction with machine learningby Ekbal Rashid.
其他題名:	emerging research and opportunities /
作者:	Rashid, Ekbal,
出版者:	Hershey, Pennsylvania :IGI Global,[2018]
面頁冊數:	1 online resource (xiv, 129 p.)
標題:	Software failuresPrevention
電子資源:	http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-3185-2
ISBN:	9781522531869 (ebook)

Enhancing software fault prediction with machine learningemerging research and opportunities /
Rashid, Ekbal,1976-

Enhancing software fault prediction with machine learningemerging research and opportunities /[electronic resource] :by Ekbal Rashid. - Hershey, Pennsylvania :IGI Global,[2018] - 1 online resource (xiv, 129 p.) - Advances in systems analysis, software engineering, and high performance computing (ASASEHPC) book series . - Advances in systems analysis, software engineering, and high performance computing (ASASEHPC) book series..

Includes bibliographical references and index.

Chapter 1. Software quality -- Chapter 2. Literature survey and scope of the present work -- Chapter 3. Overview of machine learning approaches -- Chapter 4. Methods of software quality prediction with similarity measures : as an expert system -- Chapter 5. Software quality measures as degree of excellence -- Chapter 6. Understanding the state of quality software -- Chapter 7. Estimation and evaluation of software quality at a particular stage of software development.

Restricted to subscribers or individual electronic text purchasers.

"This book presents some new ideas about estimation and evaluation of the quality of software and using a standard conversion method so that lines of code from any language may be compared and be used as a uniform metric. It covers different kinds of graphical shapes that may arise out of possible cases and gives their interpretations"--

ISBN: 9781522531869 (ebook)Subjects--Topical Terms:

808097
Software failures
--Prevention

LC Class. No.: QA76.76.F34 / R37 2018e

Dewey Class. No.: 005.3028/7

Enhancing software fault prediction with machine learningemerging research and opportunities /
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245 1 0 $a Enhancing software fault prediction with machine learning $h [electronic resource] : $b emerging research and opportunities / $c by Ekbal Rashid.
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300 $a 1 online resource (xiv, 129 p.)
490 1 $a Advances in systems analysis, software engineering, and high performance computing (ASASEHPC) book series
504 $a Includes bibliographical references and index.
505 0 $a Chapter 1. Software quality -- Chapter 2. Literature survey and scope of the present work -- Chapter 3. Overview of machine learning approaches -- Chapter 4. Methods of software quality prediction with similarity measures : as an expert system -- Chapter 5. Software quality measures as degree of excellence -- Chapter 6. Understanding the state of quality software -- Chapter 7. Estimation and evaluation of software quality at a particular stage of software development.
506 $a Restricted to subscribers or individual electronic text purchasers.
520 3 $a "This book presents some new ideas about estimation and evaluation of the quality of software and using a standard conversion method so that lines of code from any language may be compared and be used as a uniform metric. It covers different kinds of graphical shapes that may arise out of possible cases and gives their interpretations"-- $c Provided by publisher.
650 0 $a Software failures $x Prevention $x Data processing. $3 808097
650 0 $a Computer software $x Quality control $x Data processing. $3 808098
650 0 $a Software maintenance $x Data processing. $3 808099
650 0 $a Computer system failures. $3 509219
650 0 $a Computer software $x Reliability. $3 199854
650 0 $a Machine learning. $3 188639
830 0 $a Advances in systems analysis, software engineering, and high performance computing (ASASEHPC) book series. $3 808091
856 4 0 $u http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-3185-2