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Software Engineering in the Era of Cloud Computing

Cloud software engineering course that turns “I code” into “I lead” with hands-on, high-impact learning.

(SOFTENGG-CC.AU1) / ISBN : 978-1-64459-720-0
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About This Course

Enroll in our Cloud Software Development course and master the skills to build, deploy, and secure modern cloud applications…hands-on, from day one.

In this course, dive into requirements engineering for cloud computing, migrating monoliths to microservices, and machine learning in the cloud. Learn how top professionals tackle real-world challenges from security risks to green software testing using Azure and other cutting-edge tools.

You’ll get lab-driven practice with industry-relevant projects, so you can stop just reading about cloud development and start doing it.

Skills You’ll Get

  • Cloud-Native Requirements Engineering: Master frameworks like REF-SCC to gather and analyze requirements for cloud applications.
  • Microservices Migration Strategy: Learn to transition from monolithic architectures to scalable cloud-based microservices.
  • Secure Cloud Software Design: Identify and mitigate security risks in cloud-based systems through systematic review and best practices.
  • ML-Powered Cloud Development: Apply machine learning techniques (like sentiment analysis and defect prediction) using Azure and cloud platforms.
  • Cloud Testing & DevOps Optimization: Implement green software testing, virtualization, and CI/CD pipelines for efficient cloud deployments.
  • Big Data Engineering in the Cloud: Process and analyze large-scale datasets using cloud-native tools and distributed systems.
Download Course Outline

1

Introduction

  • Overview
  • Objectives
  • Organization
2

Requirements Engineering Framework for Service and Cloud Computing (REF-SCC)

  • Introduction
  • BPMN as Requirements Engineering Method
  • BPMN Requirements Engineering Life Cycle for Service and Cloud Computing (BPMN-RELC-SCC)
  • BPMN Combined Infrastructure Overview
  • Requirements Engineering Framework for Service and Cloud Computing (REF-SCC)
  • Reference Architecture for Service and Cloud Computing
  • Experimental Validation
  • Conclusion
3

Toward an Effective Requirement Engineering Approach for Cloud Applications

  • Introduction
  • Related Work
  • Cloud Application Evolution
  • Key Drivers of Cloud Applications
  • Cloud Applications Requirements Engineering
  • Cloud Application Qualities and Requirements
  • Enabling Technologies for SaaS Qualities
  • Conclusion
4

Requirements Engineering for Large-Scale Big Data Applications

  • Introduction
  • Research Methodology Using Systematic Literature Review
  • Related Work
  • Requirements Engineering for Big Data
  • Conclusion and Future Work
5

Migrating from Monoliths to Cloud-Based Microservices: A Banking Industry Example

  • Introduction
  • Monolithic Applications: Background and Challenges
  • Microservices: A Cloud-Based Alternative
  • Building Cloud-Based Applications
  • Transitioning from Monoliths to Cloud-Based Microservices
  • Conclusion
6

Cloud-Enabled Domain-Based Software Development

  • Introduction
  • Background
  • Motivation and Related Work
  • Suggested Development Paradigm
  • Discussion
  • Conclusion
7

Security Challenges in Software Engineering for the Cloud: A Systematic Review

  • Introduction
  • Motivation
  • Related Works
  • Methodology
  • Results
  • Conclusion and Future Work
8

Software Engineering Framework for Software Defe...ent Using Machine Learning Techniques with Azure

  • Introduction
  • Machine Learning Application to Software Engineering Analytics: Literature Review
  • Machine/Deep Learning Approaches to Software Engineering
  • Software Engineering Analytics Using Big Data
  • Software Defects
  • Software Defect Detection Techniques and Tools
  • Bug Prediction in Software Development
  • Neural Network Approach for Bug Prediction to Estimate Software Costs and to Feed New Requirements
  • Service-Oriented Approach to Providing Bug Prediction
  • Cloud Software Engineering for Machine Learning Applications
  • Experiment with Microsoft Azure Machine Learning
  • Critical Evaluations of Neural Network Approache...ir Application in Software Engineering Analytics
  • Conclusion and Future Work
9

Sentiment Analysis of Twitter Data Through Machine Learning Techniques

  • Introduction
  • Literature Review
  • Methodology
  • Results
  • Conclusions and Future Research
10

Connection Handler: A Design Pattern for Recovery from Connection Crashes

  • Introduction
  • Related Work
  • General Design of a Connection-Oriented Application
  • Connection Handler Design Pattern
  • Design of Reliable Applications Using the Connection Handler Design Pattern
  • Experimental Evaluation
  • Conclusion
11

A Modern Perspective on Cloud Testing Ecosystems

  • Introduction
  • Cloud Testing
  • Cloud Testing and Deployment Models
  • Tools and Frameworks for Cloud Testing
  • Conclusion
12

Towards Green Software Testing in Agile and DevO...loud Virtualization for Environmental Protection

  • Introduction
  • Cloud Computing and Services on the Cloud
  • Green Computing
  • Green Software Testing on the Cloud
  • Cloud Vendors’ Provision of TaaS
  • Green Testing on the Cloud: Agile and DevOps Software Development
  • Conclusion
13

Machine Learning as a Service for Software Process Improvement

  • Introduction
  • Overview of Software Process Improvement
  • Measurable Metrics for SPI
  • Overview of Machine Learning
  • Qualitative Research
  • Development of the Maturity Model
  • Prototype Development
  • Evaluation
  • Conclusion and Further Research
14

Comparison of Data Mining Techniques in the Cloud for Software Engineering

  • Introduction
  • Related Works
  • Materials and Methods
  • Experimental Studies
  • Conclusion

1

Requirements Engineering Framework for Service and Cloud Computing (REF-SCC)

  • In a professional meetup, four business analysts Alice, Bob, Charlie, and Diana guide a learning ...
2

Toward an Effective Requirement Engineering Approach for Cloud Applications

  • Four professionals are seated in a modern confer...olved and what drives their widespread adoption.
3

Requirements Engineering for Large-Scale Big Data Applications

  • In a relaxed yet professional cafe, four tech ex...is knowledge-sharing approach in future meetups.
4

Migrating from Monoliths to Cloud-Based Microservices: A Banking Industry Example

  • A group of four professionals convenes in a mode...ata in enhancing software engineering processes.
5

Cloud-Enabled Domain-Based Software Development

6

Security Challenges in Software Engineering for the Cloud: A Systematic Review

7

Software Engineering Framework for Software Defe...ent Using Machine Learning Techniques with Azure

  • It is a sunny Saturday afternoon at a bustling c...suggests they explore it together, step by step.
8

Sentiment Analysis of Twitter Data Through Machine Learning Techniques

  • Performing Sentiment Analysis
9

Connection Handler: A Design Pattern for Recovery from Connection Crashes

10

A Modern Perspective on Cloud Testing Ecosystems

11

Towards Green Software Testing in Agile and DevO...loud Virtualization for Environmental Protection

  • In a modern tech hub, a group of software profes...;s a collaborative knowledge-sharing experience.
12

Machine Learning as a Service for Software Process Improvement

13

Comparison of Data Mining Techniques in the Cloud for Software Engineering

  • Inside a conference room, Alex, a software engin...lenge, and clarify one another through dialogue.

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They’re cousins. Cloud computing uses software engineering principles but focuses on scalable, distributed systems. This course covers both.

Love building infrastructure? Focus on cloud computing. Prefer pure coding? Stick with software engineering. 

Both pay well, but cloud roles are exploding right now.

Absolutely. Cloud computing skills are in crazy demand right now, and companies are paying top dollar for people who know their way around AWS, Azure, or GCP.

Start with coding basics (Python’s a safe bet), get comfy with a major cloud engineering platform, and dive into real projects. Hands-on labs like the ones in this course are your best friends.

To become a certified cloud engineer, you'll need cloud platform skills (AWS/Azure/GCP), infrastructure-as-code know-how (like Terraform), security chops, and problem-solving for big, scalable systems.

Expect to earn an average annual salary of $ 110,000-$160,000 in the beginning. Get good, and you’ll clear $200K, especially with the right certifications.

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