Description
Book SynopsisThis book focuses on the development and implementation of cloud-based, complex software that allows parallelism, fast processing, and real-time connectivity. Software engineering (SE) is the design, development, testing, and implementation of software applications, and this discipline is as well developed as the practice is well established whereas the Cloud Software Engineering (CSE) is the design, development, testing, and continuous delivery of service-oriented software systems and applications (Software as a Service Paradigm). However, with the emergence of the highly attractive cloud computing (CC) paradigm, the tools and techniques for SE are changing. CC provides the latest software development environments and the necessary platforms relatively easily and inexpensively. It also allows the provision of software applications equally easily and on a pay-as-you-go basis. Business requirements for the use of software are also changing and there is a need for applications in big data analytics, parallel computing, AI, natural language processing, and biometrics, etc. These require huge amounts of computing power and sophisticated data management mechanisms, as well as device connectivity for Internet of Things (IoT) environments. In terms of hardware, software, communication, and storage, CC is highly attractive for developing complex software that is rapidly becoming essential for all sectors of life, including commerce, health, education, and transportation.
The book fills a gap in the SE literature by providing scientific contributions from researchers and practitioners, focusing on frameworks, methodologies, applications, benefits and inherent challenges/barriers to engineering software using the CC paradigm.
Table of ContentsPart 1 - Cloud Requirements Engineering and Domain Modelling: 1. Requirement Engineering Framework for Service and Cloud Computing (REF-SCC).- 2. Towards an Effective Requirement Engineering Approach for Cloud Applications.- 3. Requirements Engineering for Large-Scale Big Data Applications.- 4. Migrating from Monoliths to Cloud-Based Microservices: A Banking Industry Example.- 5. Cloud Enabled Domain Based Software Development.- 6. Security Challenges in Software Engineering for the Cloud: A Systematic Review. Part 2 - Cloud Design and Software Engineering Analytics with Machine Learning Approaches : 7. Software Engineering Framework for Software Defect Management Using Machine Learning Techniques with Azure.- 8. Sentiment Analysis of Twitter Data Through Machine Learning Techniques.- 9. Connection Handler: A Design Pattern for Recovery from Connection Crashes. Part 3 : Cloud Testing and Software Process Improvement as a Service : 10. A Modern Perspective on Cloud Testing Ecosystems.- 11. Towards Green Software Testing in Agile and DevOps using Cloud Virtualization for Environmental Protection.- 12. Machine Learning as a Service for Software Process Improvement.- 13. Comparison of Data Mining Techniques in the Cloud for Software Engineering.
