Visit This Web URL https://masterytrail.com/product/accredited-expert-level-ibm-test-automation-services-advanced-video-course Lesson 1: Strategic Test Automation Planning and Roadmapping
1.0. Introduction to Expert-Level Test Automation Strategy
1.1. Aligning Test Automation with Business Objectives and Digital Transformation
1.2. Assessing the Current State of Test Automation Maturity
1.3. Defining a Future State Vision and Goals for IBM Test Automation
1.4. Developing a Phased Test Automation Roadmap with IBM Tools
1.5. Identifying Key Performance Indicators (KPIs) for Test Automation Success
1.6. Calculating and Communicating the Return on Investment (ROI) of Advanced Automation
1.7. Establishing a Governance Model for Enterprise Test Automation
1.8. Managing Organizational Change and Adoption of New Practices
1.9. Continuous Improvement Planning for Test Automation Strategy
1.10. Capstone: Developing a High-Level Automation Strategy for a Complex Scenario
Lesson 2: Advanced Framework Design and Architecture with IBM Tools
2.0. Deep Dive into Scalable Test Automation Frameworks
2.1. Evaluating Different Framework Architectures (Data-Driven, Keyword-Driven, Hybrid, BDD)
2.2. Designing a Modular and Maintainable Framework using IBM DevOps Test
2.3. Implementing Reusability and Abstraction in Test Assets
2.4. Integrating External Libraries and Tools within the IBM Ecosystem
2.5. Version Control and Collaboration Strategies for Framework Development
2.6. Designing for forskellige Application Types (Web, Mobile, API, Desktop, Mainframe)
2.7. Implementing Robust Error Handling and Reporting Mechanisms
2.8. Performance Considerations in Framework Design
2.9. Security Best Practices in Test Automation Frameworks
2.10. Capstone: Designing a Framework Module for a Specific Application Type
Lesson 3: Mastering IBM DevOps Test UI for Complex Applications
3.0. Advanced Techniques for UI Automation with IBM DevOps Test UI
3.1. Handling Dynamic Objects and Complex UI Structures
3.2. Implementing Effective Synchronization Strategies
3.3. Utilizing Advanced Object Recognition Techniques
3.4. Developing Reusable and Robust UI Test Scripts
3.5. Integrating UI Automation with Data Management
3.6. Cross-Browser and Multi-Device UI Testing Strategies
3.7. Handling challenging UI elements (e.g., Iframes, Pop-ups, Grids)
3.8. Integrating UI Tests into CI/CD Pipelines
3.9. Advanced Reporting and Analysis for UI Test Results
3.10. Capstone: Automating a Complex End-to-End Business Flow
Lesson 4: Expert-Level IBM DevOps Test Performance for Scalability
4.0. Advanced Performance Testing Concepts and Implementation with IBM DevOps Test Performance
4.1. Planning and Designing High-Volume Performance Tests
4.2. Scripting Advanced User Scenarios and Workloads
4.3. Utilizing Advanced Data Correlation and Parameterization
4.4. Distributed Load Generation and Monitoring
4.5. Analyzing Performance Test Results and Identifying Bottlenecks
4.6. Integrating Performance Testing into the DevOps Lifecycle
4.7. Performance Testing for Microservices and Cloud-Native Applications
4.8. Capacity Planning and Scalability Testing
4.9. Troubleshooting and Optimizing Performance Test Execution
4.10. Capstone: Executing and Analyzing a Large-Scale Performance Test
Module 2: Advanced IBM Test Automation Techniques
Lesson 5: Deep Dive into IBM DevOps Test Integration and API Testing
5.0. Advanced Strategies for API and Integration Testing
5.1. Designing Comprehensive API Test Suites
5.2. Automating RESTful and SOAP API Testing with IBM DevOps Test Integration
5.3. Handling Complex Data Structures and Payloads
5.4. Implementing API Test Data Management
5.5. Mocking and Stubbing External Services for Integration Testing
5.6. Security Testing of APIs (e.g., Authentication, Authorization)
5.7. Performance Testing of APIs
5.8. Integrating API Tests into CI/CD Pipelines
5.9. Advanced Reporting and Analysis for API Test Results
5.10. Capstone: Building an Automated API Test Suite for a Microservice
Lesson 6: Service Virtualization with IBM DevOps Test Virtualization
6.0. Leveraging Service Virtualization for Enhanced Testability
6.1. Identifying Candidates for Service Virtualization
6.2. Creating and Deploying Virtual Services using IBM DevOps Test Virtualization
6.3. Simulating Complex System Behaviors and Dependencies
6.4. Managing and Versioning Virtual Services
6.5. Integrating Service Virtualization into the Test Automation Workflow
6.6. Using Service Virtualization for Performance and Resilience Testing
6.7. Troubleshooting and Debugging Virtual Services
6.8. Measuring the Impact of Service Virtualization on Testing Efficiency
6.9. Advanced Use Cases for Service Virtualization
6.10. Capstone: Virtualizing a Key External Dependency for a System Under Test
Lesson 7: Advanced Test Data Management for Automation
7.0. Strategies for Effective Test Data Management in Automated Testing
7.1. Identifying and Classifying Test Data Needs
7.2. Generating Realistic and Representative Test Data
7.3. Masking and Securing Sensitive Test Data
7.4. Managing Test Data across Different Environments
7.5. Integrating Test Data Management Tools with IBM DevOps Test
7.6. Versioning and Archiving Test Data
7.7. Data Refresh and Cleanup Strategies
7.8. Test Data Management for Parallel and Distributed Testing
7.9. Compliance and Regulatory Considerations for Test Data
7.10. Capstone: Implementing a Test Data Management Plan for an Application
Lesson 8: Integrating Test Automation into IBM DevOps Pipelines
8.0. Seamless Integration of Test Automation within the DevOps Lifecycle
8.1. Understanding the Role of Test Automation in Continuous Integration
8.2. Implementing Automated Test Execution in CI Tools (e.g., Jenkins, GitLab CI, Azure DevOps)
8.3. Orchestrating Test Execution in Continuous Delivery Pipelines
8.4. Utilizing IBM UrbanCode Deploy for Automated Test Deployment and Execution
8.5. Triggering Tests Based on Code Changes and Build Status
8.6. Analyzing and Reporting Test Results within the CI/CD Pipeline
8.7. Implementing Quality Gates Based on Automated Test Outcomes
8.8. Strategies for Fast and Reliable Pipeline Execution
8.9. Troubleshooting CI/CD Pipeline Integration Issues
8.10. Capstone: Configuring a CI/CD Pipeline to Include Automated Tests
Lesson 9: AI and Machine Learning in IBM Test Automation
9.0. Leveraging AI and ML for Smarter Test Automation
9.1. Introduction to AI/ML Concepts in Software Testing
9.2. Exploring AI-Powered Features in IBM DevOps Test (e.g., Self-Healing Tests)
9.3. Using Machine Learning for Test Case Prioritization and Optimization
9.4. Anomaly Detection in Test Results using AI
9.5. Predictive Analytics for Identifying Potential Defects
9.6. AI for Test Data Generation and Management
9.7. Natural Language Processing (NLP) for Test Case Creation (BDD)
9.8. Ethical Considerations and Bias in AI for Testing
9.9. Future Trends in AI and Test Automation
9.10. Capstone: Experimenting with an AI-Powered Test Automation Feature
Lesson 10: Advanced Reporting and Analytics for Test Automation
10.0. Gaining Actionable Insights from Test Automation Results
10.1. Designing Comprehensive Test Automation Reports
10.2. Utilizing IBM Engineering Test Management (ETM) for Test Reporting and Analysis
10.3. Integrating Test Results from Multiple Tools
10.4. Creating Custom Dashboards and Visualizations
10.5. Analyzing Trends and Identifying Areas for Improvement
10.6. Measuring Test Automation Coverage and Effectiveness
10.7. Communicating Test Results to Stakeholders
10.8. Implementing Root Cause Analysis for Test Failures
10.9. Leveraging Analytics for Predictive Quality
10.10. Capstone: Creating a Comprehensive Test Automation Dashboard
Module 3: IBM Test Automation in Enterprise Contexts
Lesson 11: Test Automation for IBM Cloud Pak for Business Automation
11.0. Strategies for Automating Testing within IBM Cloud Pak for Business Automation
11.1. Understanding the Architecture of Cloud Pak for Business Automation
11.2. Identifying Testable Components (Workflow, Decisions, Capture, RPA)
11.3. Utilizing IBM DevOps Test for Cloud Pak Component Testing
11.4. Automating Workflow and Process Testing
11.5. Testing Business Rules and Decisions
11.6. Automating RPA Bot Testing
11.7. Performance and Scalability Testing for Cloud Pak Deployments
11.8. Integration Testing with External Systems
11.9. Monitoring and Troubleshooting Tests in a Cloud Pak Environment
11.10. Capstone: Designing a Test Automation Approach for a Cloud Pak Solution
Lesson 12: Test Automation for APIs and Microservices on IBM Cloud
12.0. Automating Testing for Cloud-Native Applications on IBM Cloud
12.1. Testing Strategies for Microservices Architecture
12.2. Utilizing IBM Cloud Services for Test Environment Provisioning
12.3. Automating API Testing for Microservices
12.4. Contract Testing for Microservices
12.5. Performance Testing of Microservices at Scale
12.6. Chaos Engineering for Microservices Resilience
12.7. Integrating Test Automation with IBM Cloud DevOps Services
12.8. Monitoring and Logging for Cloud-Native Tests
12.9. Cost Optimization for Cloud-Based Test Environments
12.10. Capstone: Implementing Automated Testing for a Microservices Application on IBM Cloud
Lesson 13: Test Automation for Legacy and Mainframe Systems with IBM Tools
13.0. Strategies for Automating Testing of Traditional Systems
13.1. Understanding the Challenges of Legacy System Test Automation
13.2. Utilizing IBM DevOps Test capabilities for Mainframe Testing (e.g., 3270)
13.3. Automating Batch Process Testing
13.4. Data Migration Testing for Legacy Systems
13.5. Performance Testing of Mainframe Applications
13.6. Integrating Legacy System Tests into a Modern DevOps Pipeline
13.7. Service Virtualization for Mainframe Dependencies
13.8. Test Data Management for Legacy Systems
13.9. Collaboration Between Mainframe and Distributed Teams
13.10. Capstone: Automating a Test Scenario on a Simulated Mainframe Environment
Lesson 14: Advanced Mobile Test Automation with IBM Tools (if applicable)
14.0. Expert Techniques for Mobile Application Test Automation
14.1. Strategies for Testing Native, Hybrid, and Mobile Web Applications
14.2. Utilizing IBM DevOps Test capabilities for Mobile Testing
14.3. Handling Mobile Gestures and Interactions
14.4. Cross-Device and Cross-Platform Mobile Testing
14.5. Mobile Performance and Battery Consumption Testing
14.6. Mobile Security Testing Basics
14.7. Integrating Mobile Tests into CI/CD Pipelines
14.8. Cloud-Based Mobile Testing Platforms
14.9. Troubleshooting Mobile Test Automation Issues
14.10. Capstone: Automating a Test Case on a Mobile Emulator/Device
Lesson 15: Test Automation for Enterprise Applications (e.g., SAP, Oracle) with IBM Adapters
15.0. Automating Testing for Packaged Enterprise Applications
15.1. Understanding the Challenges of ERP System Testing
15.2. Utilizing IBM DevOps Test Adapters for Enterprise Applications
15.3. Automating Business Process Flows in ERP Systems
15.4. Test Data Management for Enterprise Applications
15.5. Performance Testing of Integrated ERP Solutions
15.6. Regression Testing Strategies for ERP Updates
15.7. Collaboration with Functional Experts
15.8. Integrating ERP Tests into the Overall Test Strategy
15.9. Troubleshooting Adapter-Based Automation
15.10. Capstone: Automating a Key Transaction in an Enterprise Application
Module 4: Advanced Practices and Specializations
Lesson 16: Security Testing Automation Integration
16.0. Integrating Automated Security Testing into the Development Lifecycle
16.1. Understanding Common Web Application Security Vulnerabilities
16.2. Utilizing Security Testing Tools (e.g., HCL AppScan) within an Automated Workflow
16.3. Automating Dynamic Application Security Testing (DAST)
16.4. Automating Static Application Security Testing (SAST) Integration
16.5. API Security Testing Automation
16.6. Incorporating Security Scans into CI/CD Pipelines
16.7. Analyzing and Reporting Automated Security Test Results
16.8. Collaboration Between Test and Security Teams
16.9. Continuous Security Monitoring
16.10. Capstone: Integrating a Security Scan into a CI/CD Pipeline
Lesson 17: Performance Engineering and Site Reliability Testing
17.0. Moving Beyond Performance Testing to Performance Engineering
17.1. Understanding Site Reliability Engineering (SRE) Principles
17.2. Defining Performance SLAs and SLOs
17.3. Proactive Performance Monitoring and Analysis
17.4. Implementing Automated Performance Regression Testing
17.5. Load Testing and Stress Testing for Resilience
17.6. Chaos Engineering and Failure Injection
17.7. Utilizing AI/ML for Performance Anomaly Detection
17.8. Capacity Planning and Auto-Scaling Validation
17.9. Performance Optimization Techniques
17.10. Capstone: Designing a Site Reliability Testing Strategy
Lesson 18: Data-Driven and AI-Powered Test Data Generation
18.0. Advanced Techniques for Generating Realistic Test Data
18.1. Utilizing Data Virtualization for Test Data Provisioning
18.2. Generating Synthetic Test Data using AI and Machine Learning
18.3. Data Subsetting and Masking for Privacy Compliance
18.4. Managing Large Volumes of Test Data
18.5. Test Data Management in Cloud and Hybrid Environments
18.6. Integrating Data Generation into Automated Workflows
18.7. Ensuring Data Consistency Across Dependent Systems
18.8. Monitoring and Auditing Test Data Usage
18.9. Future Trends in Test Data Management
18.10. Capstone: Implementing an Automated Test Data Generation Process
Lesson 19: Advanced Test Environment Management and Provisioning
19.0. Strategies for Managing Complex Test Environments
19.1. Infrastructure as Code (IaC) for Environment Provisioning (e.g., Terraform, Ansible)
19.2. Utilizing Containerization (Docker, Kubernetes) for Test Environments
19.3. Cloud-Based Test Environment Management on IBM Cloud
19.4. Managing Test Environment Configurations
19.5. Automated Environment Setup and Teardown
19.6. Monitoring and Maintaining Test Environment Health
19.7. Cost Optimization for Test Environments
19.8. Security Considerations for Test Environments
19.9. Disaster Recovery for Test Environments
19.10. Capstone: Automating the Provisioning of a Test Environment
Lesson 20: Continuous Testing and Delivery Pipelines Optimization
20.0. Optimizing Continuous Testing within CD Pipelines
20.1. Analyzing and Improving Pipeline Performance
20.2. Reducing Test Execution Time
20.3. Implementing Parallel and Distributed Test Execution
20.4. Managing Test Dependencies in Pipelines
20.5. Utilizing Feature Flags for Progressive Rollouts and Testing
20.6. Blue/Green Deployments and Canary Releases with Automated Testing
20.7. Automated Rollback Strategies Based on Test Failures
20.8. Monitoring and Alerting for Pipeline Issues
20.9. Measuring and Improving DevOps Metrics Related to Testing
20.10. Capstone: Optimizing an Existing CI/CD Pipeline for Faster Feedback
Module 5: Expert Strategies and Leadership
Lesson 21: Designing and Implementing an Enterprise Test Automation Center of Excellence (CoE)
21.0. Establishing and Maturing a Test Automation CoE
21.1. Defining the Mission and Objectives of the CoE
21.2. Structuring the CoE Team and Roles
21.3. Establishing Standards, Guidelines, and Best Practices
21.4. Implementing a Framework for Tool Evaluation and Selection
21.5. Providing Training and Mentoring Programs
21.6. Fostering Collaboration and Knowledge Sharing
21.7. Measuring the Effectiveness and Impact of the CoE
21.8. Scaling the CoE Across the Organization
21.9. Continuous Improvement of CoE Operations
21.10. Capstone: Developing a Proposal for a Test Automation CoE
Lesson 22: Advanced Troubleshooting and Debugging in Test Automation
22.0. Expert Techniques for Diagnosing and Resolving Test Automation Issues
22.1. Identifying and Analyzing Common Automation Failures
22.2. Utilizing Debugging Tools and Techniques in IBM DevOps Test
22.3. Analyzing Logs and Error Reports Effectively
22.4. Troubleshooting Environment-Related Issues
22.5. Debugging Performance and Synchronization Problems
22.6. Identifying and Resolving Test Data Issues
2.7. Utilizing Monitoring Tools for Diagnosis
22.8. Collaborative Debugging Strategies
22.9. Learning from Past Failures and Implementing Preventative Measures
22.10. Capstone: Debugging a Complex and Persistent Test Failure
Lesson 23: Measuring and Improving Test Automation ROI and Business Value
23.0. Quantifying the Value of Test Automation
23.1. Defining and Tracking Key Metrics for ROI Calculation
23.2. Demonstrating Cost Savings Through Reduced Manual Effort
23.3. Measuring the Impact on Release Speed and Frequency
23.4. Quantifying Defect Reduction and Improved Quality
23.5. Communicating Business Value to Stakeholders
23.6. Benchmarking Against Industry Standards
23.7. Identifying Opportunities for Further Optimization and Cost Reduction
23.8. Presenting a Business Case for Continued Investment
23.9. Storytelling with Data: Presenting Results Effectively
23.10. Capstone: Preparing a Business Value Presentation
Lesson 24: Leading and Mentoring Test Automation Teams
24.0. Developing Leadership Skills for Test Automation Experts
24.1. Building High-Performing Automation Teams
24.2. Mentoring Junior Automation Engineers
24.3. Fostering a Culture of Quality and Automation
24.4. Conflict Resolution and Team Dynamics
24.5. Setting Goals and Providing Feedback
24.6. Encouraging Innovation and Continuous Learning
24.7. Managing Stakeholder Expectations
24.8. Delegating Effectively
24.9. Recognizing and Rewarding Team Achievements
24.10. Capstone: Developing a Mentoring Plan for an Automation Engineer
Lesson 25: Future Trends in IBM Test Automation
25.0. Staying Ahead of the Curve in Test Automation
25.1. Exploring the Impact of AI and Machine Learning Advancements
25.2. The Role of Low-Code/No-Code Platforms in Test Automation
25.3. Testing in the Era of Serverless and Edge Computing
25.4. The Evolution of API and Integration Testing
25.5. The Growing Importance of Security and Performance Engineering
25.6. The Impact of Quantum Computing on Software Testing (Emerging)
25.7. Exploring New IBM Offerings and Roadmaps
25.8. Participating in the IBM Test Automation Community
25.9. Continuous Learning and Skill Development Strategies
25.10. Capstone: Researching and Presenting on an Emerging Trend
Module 6: Specialized IBM Test Automation Scenarios
Lesson 26: Test Automation for Data Warehousing and Big Data
26.0. Automating Testing for Data-Intensive Applications
26.1. Understanding Data Warehouse and Big Data Architectures
26.2. Utilizing IBM Data Testing Tools (e.g., Databand)
26.3. Automating Data Validation and Quality Checks
26.4. Testing ETL/ELT Processes
26.5. Performance Testing of Data Pipelines
26.6. Test Data Management for Big Data
26.7. Integration Testing with Data Sources and Consumers
26.8. Monitoring Data Quality in Production
26.9. Troubleshooting Data-Related Test Failures
26.10. Capstone: Designing an Automated Data Validation Process
Lesson 27: Test Automation for AI and Machine Learning Models
27.0. Testing the Untestable: Automating AI/ML Model Validation
27.1. Understanding the Challenges of Testing AI/ML
27.2. Strategies for Testing Model Accuracy and Performance
27.3. Automating Data Integrity and Bias Testing
27.4. Testing AI/ML Model Deployments
27.5. Monitoring AI/ML Model Behavior in Production
27.6. Utilizing IBM Watson OpenScale for Model Validation
27.7. Explainability and Interpretability of AI/ML Tests
27.8. Adversarial Testing of AI/ML Models
27.9. Ethical Considerations in AI/ML Testing
27.10. Capstone: Designing a Test Strategy for an AI/ML Model
Lesson 28: Test Automation for Blockchain Applications
28.0. Automating Testing for Distributed Ledger Technologies
28.1. Understanding Blockchain Concepts and Architecture
28.2. Identifying Testable Components of a Blockchain Application
28.3. Automating Smart Contract Testing
28.4. Testing Consensus Mechanisms
28.5. Performance and Scalability Testing of Blockchain Networks
28.6. Security Testing for Blockchain
28.7. Test Data Management for Blockchain Transactions
28.8. Integrating Blockchain Tests into CI/CD Pipelines
28.9. Monitoring and Troubleshooting Blockchain Tests
28.10. Capstone: Designing a Test Automation Approach for a Simple Blockchain Application
Lesson 29: Test Automation for IoT Solutions
29.0. Automating Testing for Internet of Things (IoT) Devices and Platforms
29.1. Understanding IoT Architectures and Communication Protocols
29.2. Identifying Testable Components in an IoT Solution
29.3. Automating Device Simulation and Testing
29.4. Testing Data Ingestion and Processing
29.5. Performance and Scalability Testing for IoT Platforms
29.6. Security Testing for IoT Devices and Data
29.7. Test Data Management for IoT Data Streams
29.8. Integrating IoT Tests into End-to-End Scenarios
29.9. Monitoring and Troubleshooting IoT Tests
29.10. Capstone: Designing a Test Automation Approach for a Basic IoT Solution
Lesson 30: Test Automation for Cloud Migration Projects
30.0. Automating Testing During Cloud Migration
30.1. Defining a Testing Strategy for Cloud Migration
30.2. Automating Functional and Non-Functional Testing in the Cloud
30.3. Data Migration Testing Automation
30.4. Performance Benchmarking Before and After Migration
30.5. Security Testing in the Cloud Environment
30.6. Testing for Cloud Service Integrations
30.7. Test Environment Management for Migration Testing
30.8. Validating Cloud-Native Features
30.9. Troubleshooting Migration-Related Test Issues
30.10. Capstone: Developing a Test Plan for a Cloud Migration Project
Module 7: Advanced Framework Development and Customization
Lesson 31: Customizing and Extending IBM DevOps Test Tools
31.0. Advanced Customization Options for IBM Test Automation Tools
31.1. Utilizing Extension Points and APIs
31.2. Developing Custom Libraries and Adapters
31.3. Integrating with Third-Party Tools and Services
31.4. Customizing Reporting and Visualization
31.5. Scripting in Advanced Languages (e.g., Java, Python)
31.6. Developing Custom Data Handling Mechanisms
31.7. Extending Object Recognition Capabilities
31.8. Contributing to Open Source Test Automation Projects (if applicable)
31.9. Best Practices for Tool Customization
31.10. Capstone: Developing a Custom Extension for an IBM DevOps Test Tool
Lesson 32: Building a Centralized Test Automation Platform
32.0. Designing and Implementing a Unified Test Automation Platform
32.1. Evaluating Different Platform Architectures
32.2. Integrating Various IBM and Open Source Tools
32.3. Implementing Centralized Test Case Management
32.4. Establishing a Shared Repository for Test Assets
32.5. Implementing Centralized Reporting and Analytics
32.6. Providing Self-Service Capabilities for Testers
32.7. Managing User Access and Permissions
32.8. Scaling the Platform for Enterprise-Wide Usage
32.9. Maintaining and Evolving the Platform
32.10. Capstone: Designing the Architecture for a Centralized Platform
Lesson 33: Integrating Test Automation with AI/ML Platforms (e.g., IBM Watson)
33.0. Leveraging External AI/ML Platforms for Enhanced Testing
33.1. Understanding the Capabilities of IBM Watson for Testing
33.2. Integrating Test Automation with Watson Services (e.g., Natural Language Understanding)
33.3. Utilizing AI for Test Case Generation from Requirements
33.4. Anomaly Detection in Application Behavior using AI
33.5. Predictive Defect Identification based on Code Analysis
33.6. AI-Powered Test Environment Monitoring
33.7. Ethical Considerations of using External AI for Testing
33.8. Data Privacy and Security with AI Integrations
33.9. Future Potential of AI Platform Integration
33.10. Capstone: Integrating a Watson Service into a Test Automation Workflow
Lesson 34: Advanced Usage of Reporting and Dashboarding Tools (e.g., Cognos Analytics)
34.0. Creating Insightful Test Automation Reports and Dashboards
34.1. Connecting Test Automation Data to Reporting Tools
34.2. Designing Interactive Dashboards for Different Stakeholders
34.3. Utilizing Advanced Visualization Techniques
34.4. Implementing Drill-Down and Filtering Capabilities
34.5. Scheduling and Distributing Reports
34.6. Integrating with Other Business Intelligence Tools
34.7. Analyzing Trends and Identifying Bottlenecks
34.8. Customizing Reports for Specific Projects or Teams
34.9. Ensuring Data Accuracy and Integrity in Reporting
34.10. Capstone: Building an Advanced Test Automation Dashboard
Lesson 35: Contributing to and Utilizing Test Automation Communities and Open Source
35.0. Engaging with the Test Automation Ecosystem
35.1. Participating in IBM Test Automation Communities and Forums
35.2. Contributing to Open Source Test Automation Projects
35.3. Leveraging Open Source Tools within an IBM Ecosystem
35.4. Sharing Knowledge and Best Practices with the Community
35.5. Learning from Community Experiences and Solutions
35.6. Collaborating on Automation Challenges
35.7. Presenting on Test Automation Topics
35.8. Staying Updated on Industry Trends Through the Community
35.9. Building Your Personal Brand as an Automation Expert
35.10. Capstone: Contributing to a Discussion or Sharing an Asset in a Community
Module 8: Emerging Topics and Expert Horizons
Lesson 36: Test Automation for Quantum Computing (Conceptual)
36.0. The Future of Testing: Exploring Quantum Computing
36.1. Introduction to Quantum Computing Concepts
36.2. Potential Impact of Quantum Computing on Software
36.3. Identifying Potential Testing Challenges for Quantum Applications
36.4. Emerging Tools and Techniques for Quantum Software Testing
36.5. The Role of Simulation in Quantum Testing
36.6. Testing Quantum Algorithms
36.7. Performance and Error Correction in Quantum Systems
36.8. Security Considerations in Quantum Computing
36.9. The Timeline for Quantum Test Automation
36.10. Capstone: Discussing the Potential Future of Quantum Test Automation
Lesson 37: Ethical Considerations in Advanced Test Automation and AI
37.0. Navigating the Ethical Landscape of Automated Testing
37.1. Bias in AI-Powered Testing and How to Mitigate It
37.2. Data Privacy and Security in Automated Testing
37.3. The Impact of Automation on the Testing Workforce
37.4. Ensuring Fairness and Equity in Automated Decision-Making (when testing AI)
37.5. Transparency and Explainability in AI Test Results
37.6. The Responsibility of the Automation Expert
37.7. Developing Ethical Guidelines for Test Automation
37.8. Staying Informed on Ethical Regulations
37.9. Promoting Ethical Practices within Teams
37.10. Capstone: Analyzing an Ethical Dilemma in Test Automation
Lesson 38: Advanced Performance Engineering with Cloud-Native Technologies
38.0. Optimizing Performance in Cloud-Native Architectures
38.1. Performance Testing Microservices and Containers
38.2. Load Balancing and API Gateway Performance Testing
38.3. Testing Serverless Function Performance
38.4. Monitoring and Alerting for Cloud-Native Performance Issues
38.5. Utilizing Cloud Provider Performance Tools (e.g., IBM Cloud Monitoring)
38.6. Chaos Engineering for Cloud-Native Resilience
38.7. Performance Testing in a Service Mesh
38.8. Cost Optimization for Cloud-Native Performance Tests
38.9. Auto-Scaling and Elasticity Testing
38.10. Capstone: Designing a Performance Testing Strategy for a Cloud-Native Application
Lesson 39: Becoming an IBM Certified Test Automation Expert
39.0. Pathways to IBM Test Automation Certification
39.1. Understanding the Certification Landscape
39.2. Preparing for Expert-Level Certification Exams
39.3. Identifying Required Skills and Knowledge
39.4. Utilizing IBM Training Resources and Documentation
39.5. Practice Exams and Preparation Strategies
39.6. Maintaining Your Certification
39.7. The Value of Certification in the Industry
39.8. Continuing Education and Skill Development
39.9. Contributing to the Certification Program (Optional)
39.10. Capstone: Creating a Personal Certification Plan
Lesson 40: Capstone Project and Expert Panel Discussion
40.0. Applying Expert-Level Knowledge to a Real-World Scenario
40.1. Project Requirements and Scope Definition
40.2. Designing an End-to-End Test Automation Solution
40.3. Implementing Advanced Automation Techniques
40.4. Integrating with Relevant IBM Tools and Platforms
40.5. Developing Comprehensive Reports and Analytics
40.6. Presenting Your Solution to an Expert Panel
40.7. Receiving Feedback and Recommendations
40.8. Discussing Industry Trends and Best Practices with Experts
40.9. Networking with Peers and Instructors