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Software Quality: The Next Big Thing in Software Engineering and Quality : 14th International Conference on Software Quality, SWQD 2022, Vienna, Austria, May 17-19, 2022, Proceedings / edited by Daniel Mendez, Manuel Wimmer, Dietmar Winkler, Stefan Biffl, Johannes Bergsmann.
- Format:
- Book
- Series:
- Computer Science (SpringerNature-11645)
- Lecture notes in business information processing 1865-1356 ; 439
- Lecture Notes in Business Information Processing, 1865-1356 ; 439
- Language:
- English
- Subjects (All):
- Software engineering.
- Software Engineering.
- Local Subjects:
- Software Engineering.
- Physical Description:
- 1 online resource (XI, 105 pages) : 25 illustrations, 23 illustrations in color.
- Edition:
- 1st ed. 2022.
- Contained In:
- Springer Nature eBook
- Place of Publication:
- Cham : Springer International Publishing : Imprint: Springer, 2022.
- System Details:
- text file PDF
- Summary:
- This book constitutes the refereed proceedings of the 14th Software Quality Days Conference, SWQD 2022, held in Vienna, Austria, during May 17-19, 2022. The Software Quality Days (SWQD) conference started in 2009 and has grown to the biggest conference on software quality in Europe. The program of the SWQD conference is designed to encompass a stimulating mixture of practical presentations and new research topics in scientific presentations. The guiding conference topic of the SWQD 2022 is "What's The Next Big Thing in Software Engineering and Quality?". The 4 full papers presented in this volume were carefully reviewed and selected from 8 submissions. The contributions were organized in two topical sections named: AI in Software Engineering; and Quality Assurance for Software-Intensive Systems. The book also contains two invited talks. .
- Contents:
- Invited Talks
- Continuous Software Engineering in the Wild
- Motivations for and benefits of adopting the Test Maturity Model integration (TMMi)
- AI in Software Engineering
- Automated Code Review Comment Classification to Improve Modern Code Reviews
- Preliminary Study on Using Text- and Image-Based Machine Learning to Predict Software Maintainability
- Quality Assurance for Software-Intensive Systems
- Specification of Passive Test Cases using an Improved T-EARS Language
- A Quality Model and Checklists for Reviewing Automotive Test Case Specifications. .
- Other Format:
- Printed edition:
- ISBN:
- 978-3-031-04115-0
- 9783031041150
- Access Restriction:
- Restricted for use by site license.
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