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Software Quality Assurance

ISBN: 978-1-118-50182-5
544 pages
December 2017, Wiley-IEEE Computer Society Press
Software Quality Assurance (1118501829) cover image

Description

This book introduces Software Quality Assurance (SQA) and provides an overview of standards used to implement SQA. It defines ways to assess the effectiveness of how one approaches software quality across key industry sectors such as telecommunications, transport, defense, and aerospace. 

  • Includes supplementary website with an instructor’s guide and solutions
  • Applies IEEE software standards as well as the Capability Maturity Model Integration for Development (CMMI)
  • Illustrates the application of software quality assurance practices through the use of practical examples, quotes from experts, and tips from the authors
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Author Information

Claude Y. Laporte, PhD, has coordinated the development, implementation and deployment of systems and software engineering processes and project management processes, and he has trained software engineers in America, Europe and Asia. Since 2000, he has been a professor at the École de technologie supérieure (ÉTS), a Canadian engineering school, where he teaches software engineering. In 2013, Professor Laporte was awarded an honorary doctorate for his contributions to software engineering. Dr. Laporte is the Project Editor of the set of ISO/IEC 29110 systems and software engineering life cycle standards and guides developed specifically for Very Small Entities (VSEs). Dr. Laporte has also written two French software engineering textbooks with Dr. April.

Alain April, PhD, is a full professor of software engineering and IT at ÉTS University, Québec, Canada. He specializes in the industry transfer of Big Data HPC applications based on Spark, H2O.ai and other cloud computing technologies applied to healthcare, construction, banking and financial industries. Professor April has been developing health care HPC applications in the area of genomic visualization, genotyping sequencing and whole genome sequencing, extending Berkeley’s Adam data structure for HPC. These applied research projects deploy large-scale machine learning algorithms in research hospitals for specific use cases: diabetes type 2 early prediction and leukemia treatments in children.

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