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AI+ Quality Assurance™ Spanish
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Introduction
Course Introduction
Audio Book: Introduction- AI+ Quality Assurance
Module 01: Introduction to Quality Assurance (QA) and AI
1.1 What is QA?
1.2 Steps in the QA Lifecycle
1.3 Types of Software Testing
1.4 Introduction to Automated Testing and its Benefits over Manual Testing
1.5 What is AI, and How can it Help in Software Testing
1.6 Simple AI Tasks: Spotting Repeated Issues Automatically
1.7 Benefits of AI in QA: Faster Testing and Finding Hidden Problems
1.8 Comparing Traditional QA with AI-Driven QA in Test Case Generation
1.9 What are QA Metrics
1.10 Basic Metrics
1.11 Automation ROI Metrics: Test Coverage and Execution Time
1.12 Introduction to Simple Data Visualization using Excel or Python
1.13 Data Sources: Test Logs, User Analytics and Historical Trends
1.14 Data Preprocessing: Cleaning, Labeling, and Augmentation
1.15 Using Labeled and Unlabeled Data for Supervised and Unsupervised Learning in Defect Prediction
1.16 Integrating Real-Time Data insights for Dynamic Testing
Module 1: Introduction to Quality Assurance (QA) and AI
Podcast: Introduction to Quality Assurance (QA) and AI
Audio Book: Introduction to Quality Assurance (QA) and AI
Activity: Hotspot
Activity: Tab
Activity: Drag and Drop
Activity: Multiple Choice
Activity: Knowledge Check
Quiz
Tools Walkthrough - Selenium
Hands - On 1
Hands - On 2
Module 02: Fundamentals of AI, ML, and Deep Learning
2.1 AI Fundamentals
2.2 Definition and History of AI From Symbolic AI to Neural Networks
2.3 Key Differences: AI, Machine Learning, and Deep Learning
2.4 Applications of AI across Industries, focusing on QA
2.5 AI Lifecycle: Problem Identification to Model Deployment
2.6 Machine Learning Basics
2.7 Machine Learning: Basic Concept and Framework of Machine Learning
2.8 Types of Machine Learning, Advantages, Limitations, and Use-Cases
2.9 Supervised Learning Algorithms in Security: Classification Algorithms
2.10 Supervised Learning Algorithms in Security: Regression Algorithms
2.11 Unsupervised Learning Algorithms in Security: Clustering Algorithms - K-Means, DBSCAN, and Hierarchical Clustering
2.12 Dimensionality Reduction Algorithms: PCA and t-SNE
2.13 Reinforcement Learning in Security
2.14 Model Evaluation Metrics
2.15 Case Study: Using Machine Learning to Analyze Customer Feedback for Quality Assurance in the Hospitality Industry
2.16 Deep Learning Overview
2.17 Introduction to Neural Networks and its Importance
2.18 Understanding Backpropagation and Its Important Parameters
2.19 Convolutional Neural Networks (CNNs) for Image Recognition
2.20 Recurrent Neural Networks (RNNs) for Sequence Data
2.21 What are Large Language Models (LLMs)
2.22 How LLMs Understand and Generate Text
2.23 Applications of LLM in QA
2.24 Limitations and Challenges of Using LLMs in QA
2.25 Case Study: Using Large Language Models (LLMs) to Automate Defect Detection and Improve Test Case Generation in Software QA
Module 2: Fundamentals of AI, ML and Deep Learning
Podcast: Fundamentals of AI, ML and Deep Learning
Audio Book: Fundamentals of AI, ML and Deep Learning
Activity: Multiple Select
Activity: Accordion
Activity: Sequence Arrangement
Activity: Problem Statement
Activity: Knowledge Check
Quiz
Tools Walkthrough - TestRigor
Hands - On
Module 03: Test Automation with AI
3.1 What is Test Automation?
3.2 Understanding Test Automation Tools
3.3 Types of Tests in Test Automation
3.4 How to Integrate Automated Tests into Continuous Integration (CI) and Continuous Delivery (CD) Pipelines?
3.5 Use Case
3.6 Introduction to AI in Test Case Generation
3.7 How AI Understands Software Requirements for Test Case Creation
3.8 Types of Test Cases Generated by AI
3.9 How AI ensures Test Coverage and finds Potential Gaps in Testing
3.10 Introduction to AI Test Automation Tools
3.11 Popular AI Automation Tools
3.12 Understanding the Process of Training AI models for Test Automation
3.13 Best Practices for Using AI Test Automation Tools
3.14 Case Study: Using AI Test Automation Tools for Mobile App Testing in a Ride-Hailing Service
3.15 Introduction to CI/CD Pipelines in Software Development
3.16 Role of AI in Dynamic Test Scheduling for CI/CD Environments
3.17 Optimizing Execution Time using Predictive Analytics
3.18 Monitoring and Feedback Loops for Continuous Improvement
3.19 Case Study: Integrating AI-Powered Test Automation in CI/CD Pipelines for a FinTech Application
Module 3: Test Automation with AI
Podcast: Test Automation with AI
Audio Book: Test Automation with AI
Activity: Hotspot
Activity: Drag and Drop
Activity: Carousel
Activity: Scenario Activity
Activity: Knowledge Check
Quiz
Tools Walkthrough - Katalon Studio
Hands - On
Module 04: AI for Defect Prediction and Prevention
4.1 What is defect prediction, and why is it important in software development
4.2 AI Models for Defect Prediction
4.3 Techniques for Defect Risk Assessment
4.4 Tools for Defect Prediction: Weka, Orange
4.5 Case Study: Predicting High-Risk Defects in E-Commerce Platforms Using Machine Learning
4.6 Introduction to Preventive QA Practices
4.7 Using AI for Preventive QA
4.8 Automated suggestion systems for code quality improvement
4.9 AI-Driven pair programming for defect prevention
4.10 Case Study: Improving Code Quality in Enterprise Applications Using AI-Powered Code Review Tool Background
4.11 Basic Concepts on Risk-Based Testing
4.12 Identifying high-risk areas using predictive analytics
4.13 Techniques for Prioritizing Test Scenarios
4.14 Dynamic Risk-Based Regression Testing
4.15 Visualizing Risk Assessment Through Heatmap
4.16 Real-time Monitoring of Test Environments
4.17 Anomaly Detection in System Performance Logs
4.18 Automated Alerts for Potential Issues
4.19 Feedback loops for Continuous quality improvement
4.20 Case Study: Proactive Issue Detection in IoT Systems Using Anomaly Detection Models
Module 4: AI for Detect Prediction and Prevention
Podcast: AI for Detect Prediction and Prevention
Audio Book: AI for Detect Prediction and Prevention
Activity: Sequence Arrangement
Activity: Case Study
Activity: Tab
Activity: True or False
Activity: Knowledge Check
Quiz
Tools Walkthrough - Mabl
Hands - On
Module 05: NLP for QA
5.1 What is NLP?
5.2 Core Concepts in NLP
5.3 How NLP is applied in QA Processes
5.4 Importance of Word Embeddings in QA tasks
5.5 Sentiment Analysis and its Relevance in User Feedback
5.6 Case Study: Extracting Test Scenarios from Requirement Documents Using NLP
5.7 Auto-Generating Test Scenarios from Requirement Documents
5.8 Using NLP for Bug Deduplication and Triaging
5.9 Log File Analysis for Anomaly Detection
5.10 Extracting and Clustering Test-Related Insights from Documentation
5.11 Case Study: Streamlining Bug Triaging Using NLP for Large-Scale Applications
5.12 Large Language Models for QA
5.13 Fine-Tuning LLMs for QA-Specific Tasks
5.14 Integrating LLMs with CI/CD Pipelines for Continuous QA
5.15 Explainability and Interpretability of LLMs in QA
5.16 How can LLMs assist in generating Bug Fix Suggestions based on Historical Bug Reports and Patches?
5.17 Case Study: Leveraging GPT-Based Models for Generating Bug Fix Recommendations
5.18 NLP for Bug Resolution and Analysis
5.19 Analyzing the Root Causes from Historical Bug Reports
5.20 Clustering and Tagging Bugs using Text Similarity Techniques
5.21 Prioritizing Fixes based on User Impact and Frequency
5.22 Automating Defect Report Summarization for Stakeholders
5.23 Case Study: Hands-On: Analyze and Cluster Bug Reports Using NLP Libraries (NLTK, SpaCy, or Hugging Face Transformers)
Module 5: NLP for QA
Podcast: NLP for QA
Audio Book: NLP for QA
Activity: Hotspot
Activity: Tab
Activity: Multiple Select
Activity: Drag and Drop
Activity: Knowledge Check
Quiz
Tools Walkthrough - SmartBear VisualTest
Hands - On
Module 06: AI for Performance Testing
6.1 Introduction to Performance Testing and its Common Types
6.2 Key Performance Metric to Measure
6.3 Overview of Manual and Traditional Automated Performance Testing Tools
6.4 Challenges in Performance Testing
6.5 Case Study: Identifying Bottlenecks in a FinTech Application Using JMeter
6.6 The Role of AI in Automating the Identification of Performance Issues and Bottlenecks
6.7 Techniques AI uses to Spot Performance Degradation
6.8 AI for Predicting Performance Under Different Loads
6.9 How to Incorporate AI Tools into your Existing Performance Testing Pipeline
6.10 Case Study: Using AI to Detect and Predict Performance Bottlenecks in a Financial Trading System.
6.11 Introduction to Performance Metrics Visualization
6.12 Common Visualization Techniques for Performance Data
6.13 Tools and Techniques for Visualizing and Interpreting Performance Metrics
6.14 Realtime Performance Metrics using Monitoring Tools
6.15 Case Study: Visualizing Realtime Performance Metrics for a Cloud-Native Application Using Grafana and Prometheus
6.16 Introduction to Predictive Load Balancing
6.17 AI Models for Predicting Test Loads
6.18 Integrating AI with CI/CD Pipelines for Load Balancing
6.19 Integration with Cloud Platforms (AWS, Azure, Google Cloud)
6.20 Case Study: AI-Driven Predictive Load Balancing for Cloud-Based Applications
Module 6: AI for Performance Testing
Podcast: AI for Performance Testing
Audio Book: AI for Performance Testing
Activity: Drag-and-drop
Activity: Sequence
Activity: Multiple Response
Activity: Problem Statement
Activity: Knowledge Check
Quiz
Tools Walkthrough - Parasoft
Hands - On
Module 07: AI in Exploratory and Security Testing
7.1 Introduction to Exploratory Testing
7.2 Tools for Automated Exploratory Testing
7.3 Role of Unsupervised Learning in Uncovering Edge Cases
7.4 Enhancing Exploratory Testing with Scenario-based AI Simulations
7.5 Case Study: Using AI for Exploratory Testing in an E-commerce Platform
7.6 Introduction to Security Testing
7.7 Automating Vulnerability Scanning using AI Tools
7.8 Penetration Testing with ML-Powered Attack Simulations
7.9 Threat Modeling and Risk Assessment using AI
7.10 Real-time Anomaly Detection for Security Breaches
7.11 Case Study: AI-Powered Vulnerability Scanning for a Healthcare Application
7.12 AI for Malware and Intrusion Detection
7.13 Leveraging Blockchain for Secure Test Environments
7.14 Automating Compliance Testing for Security Standards
7.15 Building Self-Healing Systems with AI-driven Insights
7.16 Case Study: Using AI and Blockchain for Secure Software Development in FinTech
7.17 What is Threat Analytics?
7.18 Clustering Threat Data for Early Pattern Detection
7.19 Predictive Analytics for Potential Attack Vectors
7.20 Automating Security Incident Reports with NLP
7.21 AI-based Prioritization of Security Fixes
7.22 Case Study: Clustering Threat Data to Prevent Cyberattacks in a Cloud Platform
Module 7: AI in Exploratory and Security Testing
Podcast: AI in Exploratory and Security Testing
Audio Book: AI in Exploratory and Security Testing
Activity: Drag-and-Drop
Activity: Sequence
Activity: Tab
Activity: True/False
Activity: Knowledge Check
Quiz
Tools Walkthrough - Home.Testcraft
Hands - On
Module 08: Continuous Testing with AI
8.1 Introduction to Continues Testing
8.2 Role of CI/CD Pipelines in Software Quality
8.3 Benefits of AI in Continuous Testing Environments
8.4 Real-time Feedback Loops for Defect Detection
8.5 Dynamic Test Case Prioritization based on Pipeline Data
8.6 Case Study : AI-Driven Continuous Testing in a FinTech Application
8.7 Overview of Regression Testing
8.8 Automation of Regression Suites with AI
8.9 Risk-based Selection of Test Cases in Regression Testing
8.10 Predicting the Impact of Changes using Historical Data
8.11 Adaptive Testing in Rapidly Changing Environments
8.12 Case Study: AI-Powered Regression Testing for an E-commerce Platform
8.13 Leveraging AI for Parallel Test Execution
8.14 Predictive Scaling for Testing Infrastructure
8.15 AI-powered Scheduling in Multi-Environment Testing
8.16 Integration with Cloud-based Testing Platforms
8.17 Case Study: Parallel Test Execution with AI in a Multi-Cloud Deployment
8.18 Risk Assessment Models for Continuous Integration
8.19 AI Tools for Live Defect Tracking during CI/CD
8.20 Automating Rollback Mechanisms based on Test Outcomes
8.21 Post-Deployment Monitoring with AI
8.22 Case Study: Risk-based continuous Testing for a microservices architecture in an IoT Ecosystem.
Module 8: Continuous Testing with AI
Podcast: Continuous Testing with AI
Audio Book: Continuous Testing with AI
Activity: Accordion
Activity: Hotspot
Activity: Scenario Activity
Activity: Tab
Activity: Knowledge Check
Quiz
Tools Walkthrough - Digital.ai
Hands - On
Module 09: Advanced QA Techniques With AI
9.1 Introduction to Predictive Analytics in QA
9.2 Data Collection and Preparation for Predictive Analytics
9.3 Predictive Models for Test Cycle Optimization
9.4 How do Predictive Models help prioritize Test Cases during a Test Cycle?
9.5 Test Effort and Test Coverage
9.6 Tools & Challenges in AI-Driven Test Estimations for QA
9.7 Case Study: Optimizing Test Cycles for E-commerce Platforms
9.8 Identifying Rare Bugs Using Anomaly Detection
9.9 AI Simulations for Edge-Case Scenarios
9.10 Leveraging GANs (Generative Adversarial Networks) for Synthetic Edge-Case Generation
9.11 Handling Outlier Data in QA Processes
9.12 Case Study: Using GANs to Uncover Rare Defects in a Mobile Gaming Application
9.13 Future Trends and Possibilities in AI with QA
9.14 Case Study: AI-Driven QA in E-Commerce – Amazon’s Personalized Quality Assurance
9.15 Emergence of Autonomous Testing Systems
9.16 Role of Quantum Computing in QA
9.17 Case Study: Google’s Use of Quantum Computing for Software Testing
9.18 AI-Powered Real-Time Collaboration Tools
9.19 Case Study: Microsoft Teams – AI-Driven Enhancements in Collaboration
9.20 Ethical Considerations in AI-driven QA
9.21 Case Study: Autonomous Testing for SaaS Applications
9.22 Overview of Emerging Technologies
9.23 Using AR/VR for QA in Immersive Applications
9.24 Use Case: Latency Reduction in AR Navigation for Automotive Heads-Up Displays (HUDs)
9.25 Blockchain in QA for Audit Trails
9.26 Edge Computing and its Implications for QA
9.27 Use Case: Enhancing QA Testing Accuracy in Autonomous Vehicles with Edge Computing
9.28 Use Case: Edge Computing for Scalable IoT Testing in Smart Cities
9.29 Integrating AI with IoT Testing Platforms
9.30 Use Case: AI-Powered IoT Testing in Smart Healthcare Devices
9.31 Case Study: AI-Powered QA for IoT-Enabled Smart Homes
Module 9: Advanced QA Technique with AI
Podcast: Advanced QA Technique with AI
Audio Book: Advanced QA Technique with AI
Activity: Drag-and-Drop
Activity: Tab
Activity: Sequence
Activity: Accordion
Activity: Knowledge Check
Quiz
Tools Walkthrough - Testim
Hands - On
Course Summary
Course Summary
Module 10: Capstone Project
Podcast: Capstone Project
Audio Book: Capstone Project
Tools Walkthrough - Functionize
Additional Module - AI Agents For Quality Assurance
AI Agents Quality Assurance
Resources
AI+ Quality Assurance Blueprint
AI+ Quality Assurance Detailed Curriculum
AI+ Quality Assurance Tools
AI+ Quality Assurance Resources and References
AI CERTs Exam Guidelines
AI CERTs Exam Guidelines
AI+ Quality Assurance Examination
AI+ Quality Assurance Examination
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