Software Testing Trends - QA Learning Portal

This learning path is built for software testers who want to integrate AI into their testing workflows. It moves from core AI fundamentals to advanced agentic workflows and custom tool development, and then extends into Testing AI Systems as a specialization.

Pre-requisites

A solid foundation in traditional software testing methodologies is crucial.

If you're new to software testing, consider starting with this roadmap: Software Testing Roadmap

If you want a quick overview of the roadmap, you can watch this video.

What Is an AI Engineer?

An AI engineer builds systems that use AI to solve real business problems.
This role is not data science and not model training from scratch.
Focus is on integration, orchestration, security, scalability, performance, and cost control.
AI engineers connect:
- AI models (OpenAI, Hugging Face)
- Company data (databases, files, documents)
- Company tools (email, internal services, apps)
- User interfaces

This path lets you contribute to generative AI systems and agentic workflows quickly, without spending years on computer science fundamentals or statistics.

Phase 1: Generative AI Foundations (4–6 weeks)
Phase 2: AI Agent & Agentic Workflows (6–8 weeks)
Phase 3: Custom AI Testing Tools Development (8–10 weeks)
Phase 4: Testing AI Systems (4–6 weeks)
Phase 5: Certifications (Optional)

Phase 1: Generative AI Foundations (4–6 weeks)

Generative AI Fundamentals

• AI, Gen AI, LLMs

• Tokenization

• Context Engineering

• Multimodal AI Basics

• Determinism vs randomness (temperature, top-p)

• Understanding Model Limitations

→ Hallucinations

→ Biases

→ Ethical Considerations

→ When AI Fails

Prompt Engineering

• Zero-shot, Few-shot, Chain-of-Thought

• Role prompting

• Structured outputs (JSON, schemas)

• Self-check prompts and verification patterns

• Fact grounding strategies

• Prompt Templates

Data Sensitivity Awareness

• What not to send to public models

• Redaction strategies

• Local vs cloud tradeoffs

→ When to use local models

→ Privacy, Cost, Control

Running Local Models & Open Source

Running Local Models

• Ollama

• Open WebUI

Open Source Models

• Hugging Face

Phase 2: AI Agent & Agentic Workflows (6–8 weeks)

AI Agents & Agentic AI

• Understanding AI Agents

• Agentic AI Concepts

• Agent Autonomy & Decision-Making

Agentic Code IDEs/Terminal

• VS Code + Github Copilot (Recommended)

Github Copilot Modes: Ask, Edit, Plan, Agent

Instruction Files:

→ .github/copilot-instructions.md

→ .instructions.md files

→ AGENTS.md files

Advanced: Prompt Files, Custom Agents, Agent Skills

• Gemini CLI's

• Google Antigravity

• Cursor

Agent Boundaries & Guardrails

• Tool access boundaries

• Read-only vs write permissions

• Human-in-the-loop patterns

Failure Handling & Recovery

• Agent retries and fallback logic

• Partial failures and degraded modes

• When agents should stop

Model Context Protocol (MCP)

• Core Concepts

→ What is MCP?

→ MCP Client Vs MCP Server

• 3rd Party MCP Servers

→ Github, Playwright, Atlassian

→ Chrome DevTools, Database

→ Appium, Context7, ShadCN

• Advanced Topics

→ Creating own MCP Servers

→ Debugging MCP Server

→ MCP Security Best Practices

Observability for Agents

• Logging agent decisions

• Tracing tool calls

• Auditing outputs

Phase 3: Custom AI Testing Tools Development (8–10 weeks)

RAG (Retrieval Augmented Generation)

• Embeddings

• Vector Databases

• Semantic Search

Programming Languages & Tools

• Python Basics

→ Variables, Loops, Functions

→ File Handling (JSON)

→ Package Management, Async Operations

→ Integrating with LLM APIs

→ Pandas, Numpy, Pydantic

• JavaScript Basics

→ Variables, Loops, Functions

→ Async Operations (Promises, async/await)

→ HTTP Requests (Fetch API, Axios)

→ Integrating with LLM APIs

• Package Managers

→ NPM/Yarn (Node.js)

→ UV Package Manager (Python)

• Git/Github

Vibe Engineering

• VS Code with Github Copilot

• Cursor

• Google Antigravity

AI APIs & Security

• AI Providers

→ OpenAI

→ Anthropic

→ Google

• Security & Cost Controls

→ API Rate Limiting

→ Token budgeting

→ Secrets management

AI Frameworks & Collaboration

• LangChain for QA utilities

• Multi-agent collaboration (CrewAI)

Agent Orchestration Patterns

→ Sequential vs parallel execution

→ Routing strategies

→ Error propagation across agent chains

Deployment & Monitoring

• Deployment Options

→ Cloud Providers (AWS, GCP, Azure)

→ Serverless (Lambda, Cloud Functions)

→ Containerization (Docker, Kubernetes)

• Monitoring & Cost Control

→ Performance Monitoring

→ Cost Tracking Tools

AI Security Best Practices

• OWASP AI Security Top 10

• Data Privacy Considerations

• Secure API Usage

Specialized Topics

Fine-tuning Basics

Understanding when and how to fine-tune models for specialized testing tasks

Visual Testing with AI

Vision models for screenshot comparison, UI regression detection, and visual validation

Performance & Cost Optimization

Caching strategies, batch processing, model size selection, token usage management

Recommended Projects

Requirements-to-Test-Cases Generator

Converts user stories into comprehensive test scenarios with intelligent analysis of requirements and automatic edge case identification.

Bug Report Enhancer

Takes minimal bug reports and enriches them with context, reproduction steps, and similar historical issues for faster resolution.

Synthetic Test Data Generator

Using AI to create realistic test datasets, edge cases, and boundary conditions based on schemas and business rules.

AI Release Readiness Agent

Summarizes test results, risks, and open issues to provide clear go/no-go recommendations for releases.

Flaky Test Triage Agent

Identifies flaky patterns in test execution and suggests stabilization actions to improve test reliability.

CI Failure Investigator

Correlates failures with recent commits and environment changes to quickly identify root causes in CI pipelines.

Talk to Test Artifacts

Build a RAG-based assistant that answers questions using your internal testing documentation. Feed it test plans, bug reports, requirements, and release notes. Ask it "Has this bug occurred before?" or "Which areas were high-risk last release?" This demonstrates you can make company knowledge instantly accessible.

Talk to Test Data

Create a natural language interface to your test databases. Instead of writing SQL, stakeholders ask "Show failed tests in the last 5 builds grouped by module" and get visualizations.

AI Test Communication Agent

Build an agent that monitors test signals and communicates intelligently. When critical tests fail, it notifies Slack and updates Jira. When regression passes, it sends release summaries to stakeholders. When flaky tests spike, it alerts the QA lead with trend analysis.

Visual Regression Agent

Compares screenshots and explains UI differences in natural language, making visual testing accessible to non-technical stakeholders.

Remember: Becoming an AI Test Engineer takes time and dedication. This roadmap provides a framework; your commitment and hard work will drive your success!

Phase 4: Testing AI Systems (4–6 weeks)

Testing AI systems requires a fundamentally different approach than traditional software testing. AI systems are probabilistic and non-deterministic, with behavior that emerges from training data rather than explicit rules. If you are interested in this path, check out this guide: Testing AI Systems, which covers essential concepts and practices for testing AI-powered applications.

AI Test Engineering Roadmap 2026