Master Software Testing With Gen Ai

Learn to automate, optimize, and transform QA workflows with AI-driven strategies and tools

What you'll learn

Master the principles and applications of Generative AI in software testing.

Automate test case generation and data creation with cutting-edge tools.

Integrate AI with popular frameworks like Selenium and Cypress.

Build expertise in AI-driven exploratory and performance testing

Requirements

The prerequisites for this course are: A basic understanding of Quality Assurance (QA) principles and practices. Some level of experience working as a QA professional, including familiarity with testing methodologies and tools. A willingness to explore AI-driven approaches to improve testing efficiency and effectiveness. This course is ideal for learners with foundational QA knowledge who want to enhance their skills with Generative AI in testing.

Description

Unlock the potential of Generative AI to transform your software testing skills with ArkaTalent Tech’s comprehensive course, “Master Software Testing with Gen AI: Cutting-Edge Tools.” Designed by industry leaders with years of expertise in AI testing, this course bridges the skills gap in the booming AI landscape.In today’s fast-evolving tech world, companies increasingly rely on AI-driven tools for innovation. Yet, ensuring the quality and reliability of these systems remains a challenge. This course equips you with the knowledge and hands-on skills to excel as a QA professional, using AI to revolutionize your testing workflows.You’ll begin by exploring the foundations of software testing and understanding how Generative AI integrates into QA processes. The course progresses to advanced modules covering test case generation, synthetic data creation, bug detection, exploratory testing, and more. Learn how to enhance popular frameworks like Selenium and Cypress with AI-powered capabilities and gain expertise in AI tools like OpenAI’s API, Hugging Face Transformers, and JMeter.Whether you’re a QA professional looking to upgrade your skills or a developer exploring AI in testing, this course will prepare you to lead in the next generation of software testing. Join us and stay ahead in the competitive world of AI-driven technology!

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Module 1: Foundations of Software Testing and Generative AI

Lecture 2 Software testing basics: principles, lifecycle, and methodologies

Lecture 3 Introduction to Generative AI and its role in software engineering

Lecture 4 Comparison: Traditional testing vs AI-powered testing

Lecture 5 Overview of AI technologies used in testing

Section 3: Module 2: Generative AI in Test Case Design

Lecture 6 Automating test case generation using AI models

Lecture 7 Benefits, Challenges and Considerations of Automating Test Case Generation

Lecture 8 AI in Creating edge cases and handling boundary value analysis.

Lecture 9 Identifying high-risk scenarios with AI insights.

Lecture 10 Tools: OpenAI API, Hugging Face Transformers

Section 4: Module 3: Test Data Generation and Management

Lecture 11 Generating synthetic data using Generative AI

Lecture 12 Anonymizing sensitive data for compliance

Lecture 13 Advanced Anonymization Techniques

Lecture 14 AI techniques for creating realistic, domain-specific datasets , GAN's hands on

Lecture 15 Step by Step guide for data generation using Pytorch and Tensorflow - VAE model

Lecture 16 LLM's and Diffusion models - hands on

Lecture 17 Practical session : How to use Faker, DataSynth or custom Generative AI models

Section 5: Module 4 : Bug Detection & Static Code Analysis with AI

Lecture 18 AI-driven static code analysis for vulnerability detection

Lecture 19 Identifying code smells and performance bottlenecks

Lecture 20 Tools: SonarQube, DeepCode and AI-powered linters

Section 6: Module 5 : Automating Test Execution with Generative AI

Lecture 21 Hands on : Enhancing traditional frameworks -Selenium with AI

Lecture 22 Hands on : Enhancing traditional frameworks - Cypress with AI

Lecture 23 Steps to Use AI for Flaky Test Detection

Lecture 24 Self-Healing Scripts: Adapting Tests for Dynamic UI Changes

Lecture 25 Tools: Selenium with AI Plugins, Testim.io

Section 7: Module 6: Exploratory Testing with AI

Lecture 26 AI-generated exploratory scenarios to uncover hidden issues

Lecture 27 Simulating user behavior with AI-driven agents

Lecture 28 Introduction to Reinforcement Learning Frameworks

Lecture 29 Introduction to Robotic Process Automation (RPA) Tools

Section 8: Module 7: Performance and Load Testing with Generative AI

Lecture 30 Creating intelligent performance test scenarios with AI

Lecture 31 Using AI for Predictive Performance Analysis

Lecture 32 Hands on : AI Integrations in JMeter

Lecture 33 Hands on : AI Integrations in Locust

Lecture 34 The Future of AI in Performance Testing

Section 9: Module 8: Testing APIs and Microservices with AI

Lecture 35 Automated API testing with Generative AI

Lecture 36 Using AI to Analyze API Contracts and Generate Test Cases

Lecture 37 Postman with AI Enhancements

Lecture 38 Swagger/OpenAPI with AI Enhancements

Section 10: Module 9: Continuous Testing and DevOps Integration

Lecture 39 Role of AI in CI/CD pipelines.

Lecture 40 Automating regression and acceptance tests in DevOps workflows

Lecture 41 Hands on : Jenkins with AI Integrations

Lecture 42 Hands on : GitHub Actions Integration with AI

Section 11: Module 10: Testing AI and ML Systems

Lecture 43 Testing AI & ML Systems

Lecture 44 Challenges in Functional Testing of AI and Machine Learning Models

Lecture 45 Challenges in Non-Functional Testing of AI/ML models

Lecture 46 AI Tools for Model Evaluation and Fairness

Lecture 47 Methods for Generating Synthetic Data

Section 12: Module 11: NLP Applications in Testing

Lecture 48 Introduction to NLP Applications in Software Testing

Lecture 49 Log analysis and error clustering with NLP

Lecture 50 NLP Techniques in Testing

Lecture 51 Hands on : Tools and Libraries for NLP in Testing - Stacy

Lecture 52 Hands on with NLTK

Lecture 53 Hands on with Hugging Face Model

Section 13: Module 12: Ethical and Practical Considerations in AI Testing

Lecture 54 Ethical issues: Bias, data privacy, and security concerns

Lecture 55 Limitations of Generative AI in testing

Lecture 56 Best practices for responsible AI-driven testing

Section 14: Module 13: Advanced techniques for AI-driven testing

Lecture 57 AI for dynamic test prioritization and risk-based testing

Lecture 58 Self-healing tests for frequently updated applications

Lecture 59 Emerging AI techniques for test optimization

Lecture 60 Key Takeaways

QA Professionals looking to enhance testing with Generative AI. Software Testers wanting to adopt AI-powered techniques. Developers aiming to integrate AI-driven testing into their workflows. AI Enthusiasts curious about Generative AI in software engineering. Tech Professionals addressing AI skills gaps in testing. Beginners in software testing seeking a competitive edge through AI techniques