Artificial Intelligence IT Trainer

**Training Plan for AI-Driven SDLC Testing**

**Week 0: Pre-Assessment and Program Launch**

**Duration**: 2 hours (Instructor-Led)
- **Pre-Assessment**:

- Evaluate participants' understanding of software testing, Generative AI, secure coding practices, and tool usage (e.g., Python, IDEs).
- Tools: Online quizzes or coding challenges.
- **Program Launch**:

- Overview of Generative AI and its role in SDLC testing.
- Introduction to tools and IDEs (VS Code, Visual Studio Enterprise).
- Program roadmap and goals.
- **Outcome**:

- Participants align expectations, familiarize themselves with tools, and set a baseline for learning.

**Week 1: Fundamentals of Generative AI in Testing**

**Modality**: 1 hour e-learning + 3 hours instructor-led
- **E-learning**:

- Tools: TensorFlow, PyTorch.
- **Instructor-Led Session**:

- Deep dive into AI-driven testing workflows.
- Hands-on: Setting up Python-based AI environments in VS Code.
- Practical demonstration: Generate Starter Test Cases for the solution.
- **Outcome**:

- Understand how to generate foundational test cases and integrate AI into testing workflows.

**Use Case Reference**: Generate Starter Test Cases for the Solution
**Tools**: TensorFlow, PyTorch, VS Code.

**Week 2: Test Automation for Non-Identifiable UI Elements**

**Modality**: 1 hour e-learning + 3 hours instructor-led
- **E-learning**:

- Basics of computer vision for UI automation.
- Tools: OpenCV for dynamic element recognition.
- **Instructor-Led Session**:

- Hands-on:

- Automating UI components with OpenCV and Python.
- Handling dynamic and complex UI scenarios using computer vision.
- Case Study: Automating a web form with dynamic identifiers.
- **Outcome**:

- Build automation scripts for UI testing using computer vision techniques.

**Use Case Reference**: Test Automation for Non-Identifiable UI Elements
**Tools**: OpenCV, Python.

**Week 3: Synthetic Test Data Generation**

**Modality**: 1 hour e-learning + 3 hours instructor-led
- **E-learning**:

- Privacy-compliant synthetic data generation using Generative AI.
- Introduction to scalable dataset creation tools.
- **Instructor-Led Session**:

- Hands-on:

- Generate datasets tailored for edge cases using Python and Generative AI libraries.
- Techniques to ensure privacy compliance.
- Group Activity: Create diverse test datasets for a given scenario.
- **Outcome**:

- Participants create high-quality, scalable synthetic datasets for testing.

**Use Case Reference**: Synthetic Test Data Generation Using Generative AI
**Tools**: Python libraries, Generative AI frameworks.

**Week 4: Self-Healing Test Automation**

**Modality**: 1 hour e-learning + 3 hours instructor-led
- **E-learning**:

- Basics of self-healing automation frameworks.
- Introduction to machine learning for adaptive testing.
- **Instructor-Led Session**:

- Hands-on:

- Implementing self-healing mechanisms to adapt to UI changes.
- Using ML models for script updates.
- Real-life Example: Addressing dynamic workflow updates in test cases.
- **Outcome**:

- Develop resilient automation scripts with self-healing capabilities.

**Use Case Reference**: Self-Healing Test Automation
**Tools**: Machine learning libraries, Python.

**Week 5: Secure Coding and Vulnerability Detection**

**Modality**: 1 hour e-learning + 3 hours instructor-led
- **E-learning**:

- Best practices for secure coding.
- Overview of vulnerabilities: SQL injection, XSS, insecure communication.
- **Instructor-Led Session**:

- Hands-on:

- Detecting hard-coded secrets and insecure coding patterns using AI.
- Mapping vulnerabilities to NIST guidelines.
- Activity: AI-assisted code refactoring for security compliance.
- **Outcome**:

- Strengthen secure coding practices and proactively identify vulnerabilities.

**Use Case Reference**: AI-Powered PII Anonymization
**Tools**: Context-aware AI tools, Python.

**Week 6: Penetration Testing and Regulatory Compliance**

**Modality**: 1 hour e-learning + 3 hours instructor-led
- **E-learning**:

- AI-driven penetration test scenario generation.
- Basics of GDPR and HIPAA compliance.
- **Instructor-Led Session**:

- Hands-on:

- Generating test cases for vulnerabilities like CSRF and XSS.
- Ensuring data compliance through anonymization and validation.
- **Outcome**:

- Gain practical experience in AI-powered penetration testing and regulatory compliance.

**Use Case Reference**: AI-Powered PII Anonymization
**Tools**: Generative AI libraries, Python.

**Week 7: Deployment Security and Log Analysis**

**Modality**: 1 hour e-learning + 3 hours instructor-led
- **E-learning**:

- Deployment security fundamentals.
- AI-based log anomaly detection techniques.
- **Instructor-Led Session**:

- Hands-on:

- Scanning deployment scripts for misconfigurations.
- Analyzing system logs using AI for predictive bug hunting.
- **Outcome**:

- Enhanced deployment security and proactive anomaly detection.

**Use Case Reference**: Predictive Bug Hunting
**Tools**: Machine learnin