Titlebook: Software Engineering in the Era of Cloud Computing; Muthu Ramachandran,Zaigham Mahmood Book 2020 Springer Nature Switzerland AG 2020 Softw

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Thalita Vergilio,Muthu Ramachandran,Duncan Mullier

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Toward an Effective Requirement Engineering Approach for Cloud Applicationspose a systematic requirement engineering approach to address this deficiency and facilitate building SaaS with cloud qualities in mind. The proposed cloud requirements engineering approach relies on identifying the number of cloud qualities to be utilized in SaaS and consequently addresses any exis

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Requirements Engineering for Large-Scale Big Data Applicationsrocessing). MC-BDP represents an evolution of the PaaS-BDP (Platform as a Service for Big Data Processing) architectural pattern, previously developed by the authors. However, its presentation is not within the scope of this study. The scope of this comparative study is limited to the examination of

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Migrating from Monoliths to Cloud-Based Microservices: A Banking Industry Exampleto develop solutions using a mixture of technologies. Microservices architecture aims to decompose a monolithic application into a set of independent services which communicate with each other through open APIs or highly scalable messaging. In short, microservices architecture is more suited for bui

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Cloud-Enabled Domain-Based Software Developments by defining reference architectures, process models and other assets for the application developers. Consequently, there is a possibility of a market place shaping up where such assets can be offered and consumed, subject to an administration for security and optionally commercial purposes. Open,

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Security Challenges in Software Engineering for the Cloud: A Systematic Reviewc review of articles in the area of software engineering security challenges on the cloud. The review examines articles that were published between 2014 and 2019. The procedure for article qualification relied on the elucidation of Preferred Reporting Items for Systematic Reviews and Meta-Analyses p

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Software Engineering Framework for Software Defect Management Using Machine Learning Techniques withe. The model in this chapter aims to predict the presence or absence of a bug in the code, using machine learning classification models. Using Microsoft Azure’s machine learning platform, this model can be distributed as a web service worldwide, thus providing bug prediction as a service (BPaaS).

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