SB
Spandana Bala
SDET
Hyderabad · India
Now at · Microsoft
Class 014 · TESTING & AI AGENTS · AI-POWERED QA
Testing
+ AI
Agents
Master end-to-end QA with Agentic AI. Cover manual testing fundamentals through Selenium and Playwright automation with Page Object Model, ship Python + Postman API testing with security checks, and deploy a Testing Coding Agent that heals locators and proposes remediations.
5mo
duration
35+
modules
4.7/5
class rating
100k+
enrolled
Where our Software Testing alumni work
Microsoft
Amazon
SalesforceAI Engineer
Deloitte
Infosys
Accenture
TCS
Wipro
Capgemini
Cognizant
HCL
MicrosoftAmazonSalesforceAI EngineerDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL
What you leave with
Four things every Testing grad walks away with.
01
Agent-Ready QA skills
Both halves of modern QA — manual fluency plus Selenium with POM/TestNG, Playwright with self-healing locators, Pytest API testing — and an LLM test layer with LangGraph, Claude Agent SDK, MCP.
02
A shipped project
A production-deployed Testing Coding Agent that generates test cases, heals broken locators, and triages flaky tests via MCP into your repos and CI/CD — with a public verification URL.
03
Verifiable credential
2026 Agent-Ready rubric mapped to ISTQB CTFL, CTAL-TA, Advanced Test Automation Engineer, Selenium Tester Foundation, plus the AI Testing Engineer BoK, graded 1–5, with a public verification URL.
04
Direct placement pipeline
GitHub + LinkedIn portfolio rewrite, QA-tuned resume rebuild, and warm intros into our 1,000+ hiring partners actively staffing QA, SDET, Automation, and AI Testing Engineer roles.
5 MONTHS · FOUR PHASES · ONE TESTING AGENT
From “writes test cases” toships autonomous testing agents..
Weeks 1–2 · Foundations
IT & AI Foundations + Testing Mindset
- Application lifecycle, Agile/Scrum, and cloud computing models
- Introduction to AI, ML, Generative AI, and Agentic AI
- QA mindset — quality vs testing, prevention vs detection
- V-Model, Agile Testing Quadrants, and Shift-Left vs Shift-Right
YOU SHIPA configured QA toolchain — VS Code, Git, Jira/TestRail — plus your first test plan for the LMS reference application.
Weeks 3–8 · Manual Testing (10 modules)
End-to-End Manual Testing Mastery
- Software testing fundamentals — verification vs validation, principles
- Testing levels — Unit, Integration, System, and Acceptance (UAT)
- Functional and non-functional testing across usability, performance, security
- Test design techniques and documentation with Jira, TestRail, Zephyr
YOU SHIPA manual testing portfolio for the LMS app — test plans, functional + non-functional cases, defect reports, and a full traceability matrix.
Weeks 9–14 · Automation + API Testing
Selenium + Playwright + Python API Testing
- Selenium WebDriver, Page Object Model, TestNG, and Grid execution
- Playwright with auto-waiting, AI self-healing locators, and Codegen
- API testing — REST, Postman collections, and Python requests with Pytest
- GraphQL, OWASP API Top 10, GitHub Actions, and Azure Pipelines CI/CD
YOU SHIPA Selenium + Playwright automation framework and a Python API test suite, both wired into a GitHub Actions CI/CD pipeline.
Weeks 15–20 · GenAI + Agentic AI
Master the 2026 GenAI + Agentic AI stack — and ship a Testing Coding Agent that generates test cases, heals broken locators, and triages flaky tests autonomously.
Engineer with LLM APIs from OpenAI, Anthropic, Google GenAI, and DeepSeek. Master prompt engineering (zero-shot, few-shot, CoT, ReAct) and context engineering — the 2026 frontier discipline. Build production RAG pipelines with ChromaDB and pgvector over your test repos and requirements docs. Master the 2026 production agent stack — LangGraph 1.0 (#1 production default), Claude Agent SDK (#2 MCP-native), CrewAI (#3 multi-agent crews). Wire it all through the Model Context Protocol (MCP) — 200+ server implementations, 97M+ monthly SDK downloads. Final project — a deployed Testing Coding Agent with MCP servers exposing your test repos, CI/CD pipelines, and Jira bug tracker. The QA engineer’s force multiplier.
Partner orgs
(2026)48
Testing
projects deployed290+
→ Placement
offers82%
Course curriculum
Seven sections. 65+ modules. The AI-native Software Testing stack.
01
Fundamentals of IT & AI
Foundational track building the conceptual bedrock for every QA engineer —
application lifecycle, Agile/Scrum, computing infrastructure, AI/ML/Generative/Agentic AI
fundamentals, and real-world digital systems. Sets the context for everything that follows in the
testing + AI engineering stack.
Application fundamentals — what applications are, their types, web architecture
Web Technologies — Frontend (HTML, CSS, JavaScript, React) and Backend (Python,
Java, Node.js)
Database Systems — SQL (PostgreSQL, MySQL) and NoSQL (MongoDB)
The seven SDLC phases — Planning, Analysis, Design, Implementation, Testing,
Deployment, Maintenance
Where testing fits in every SDLC phase (not just the Testing phase)
Testing is not a phase, it's a discipline practised throughout the SDLC
Methodology Evolution — Waterfall vs Agile, the Agile mindset
Popular frameworks — Scrum, Kanban, Extreme Programming (XP)
Scrum Roles — Product Owner, Scrum Master, Development Team (including testers)
Scrum Events — Sprint Planning, Daily Scrum, Sprint Review, Sprint Retrospective
Scrum Artifacts — Product Backlog, Sprint Backlog, Increment deliverables
User Stories — Epics, Themes, Acceptance Criteria — the tester's primary input
Estimating user stories and managing backlogs with Google Sheets and Azure Boards
In Agile, the tester is part of the team, not a downstream gate — quality is
everyone's responsibility
CPU Technology — general-purpose computing, sequential operations, multi-core
parallel processing
GPU Technology — parallel processing for AI training (relevant for AI testing
workloads)
IaaS — Infrastructure as a Service — testing cloud-deployed applications
PaaS — Platform as a Service — testing apps running on managed platforms
SaaS — Software as a Service — testing browser-delivered enterprise applications
Cloud testing is increasingly the default — your tests need to handle dynamic
infrastructure, scaling, and ephemeral environments
AI is reshaping QA practice in 2026 — from AI-generated test cases to
self-healing locators to autonomous testing agents
Machine Learning — algorithms that improve through experience
Deep Learning — neural networks for complex pattern recognition (visual testing
uses this)
Generative AI — systems that generate test cases, test data, and remediations
Large Language Models — LLMs that generate test cases from requirements
Agentic AI — autonomous systems that plan, reason, act, and learn — the future of
testing automation
Customer Relationship Management — CRM testing challenges (data integrity,
workflows, integrations)
Human Resource Management Systems — HRMS testing focus areas (sensitive data,
compliance)
Retail & E-Commerce — high-traffic e-commerce testing (performance, security,
payments)
Healthcare Applications — regulated workloads, patient safety, HIPAA compliance
testing
Real-world systems demonstrate why thorough testing matters — every business now
depends on software that just works
02
Manual Testing
The heart of QA fluency — 10 modules covering the entire discipline of manual
testing. From understanding why software fails through to writing the test plans, traceability
matrices, and defect reports every QA hiring panel asks for. The most comprehensive manual testing
coverage in any Indian QA training — and the foundation everything else builds on.
System Software — OS, drivers, firmware
Programming Software — compilers, IDEs, debuggers
Application Software — desktop, web, mobile apps
Software defects cost billions annually — testing ensures reliability, security,
and user satisfaction before release
Real-world failures: Therac-25 (radiation overdoses), Mars Climate Orbiter (unit
mismatch crash), Boeing 737 MAX (MCAS software flaw) demonstrate the life-critical importance of
thorough testing
SDLC Overview — Plan → Design → Develop → Test → Deploy → Maintain
QA vs QC — QA is process-oriented (prevention); QC is product-oriented
(detection)
Errors, Bugs & Defects — Error → mistake by developer; Bug → flaw in code; Defect
→ deviation from requirement; Failure → system malfunction
STLC Introduction — Software Testing Life Cycle defines structured phases from
requirement analysis to test closure
The distinction between Error, Bug, Defect, and Failure isn't pedantry — it
determines who owns the fix, how it's prioritised, and how it's reported
Waterfall Model — sequential phases, formal sign-offs, testing concentrated at
the end
V-Model — testing planned in parallel with each development phase; the
QA-favourite model
Iterative & Incremental — build → review → refine cycles
Spiral Model — risk-driven iterations
Agile Models — Scrum, Kanban, XP — testing embedded in every sprint
DevOps Testing — Shift-Left (testing earlier) and Shift-Right (production
observation) approaches
Testing approach selection — which model fits which project type
Hybrid approaches in real enterprises
Adapting testing strategy as projects evolve
What unit testing is and why developers (not QAs) typically write it
Unit testing frameworks — JUnit, NUnit, pytest, Jest
Test-Driven Development (TDD) and how QAs collaborate with TDD developers
Code coverage metrics — line, branch, function, condition coverage
Integration Testing — why components that work individually can fail together
Approaches — Big Bang vs Incremental
Incremental strategies — Top-Down (stubs), Bottom-Up (drivers), Sandwich/Hybrid
Integration testing tools and patterns
System Testing — end-to-end testing of the complete integrated system against
specified requirements
Covers both functional and non-functional aspects
GUI Testing — validates visual design, functional elements, content accuracy, and
user interaction flows across the graphical interface
Usability Testing — assesses user-friendliness, navigation intuitiveness, and
overall user satisfaction
Real user observation, think-aloud protocols, satisfaction metrics
Alpha Testing — internal testing by the organisation
Beta Testing — external testing by real end-users in production-like environments
UAT planning, sign-off, and defect management
UAT failure has nothing to do with whether the software "works" — it's about
whether the software does what the business actually needs
Object Properties Testing — verifying UI element states, attributes, and
behaviours
Database Testing — CRUD operations validation, data integrity, SQL basics for
testers
Error Handling & Calculations — validating error messages, computation accuracy,
edge cases
Links, Cookies & Sessions — internal, external, anchor & email links; cookie
attributes; session management
Test data creation strategies
Data masking for sensitive information
Test data refresh and parameterisation
Bad test data is the #1 source of false positives in functional testing — invest
in test data discipline early
Load Testing — verify system behaviour under expected user loads
Stress Testing — test system limits and breaking points
Spike Testing — validate response to sudden traffic increases
Endurance/Soak Testing — assess stability over extended periods
Volume Testing — test with large data volumes
Scalability Testing — verify system can scale to meet demand
Security Testing — Authentication, Authorisation, Encryption (the AAE triangle)
Session management security
Input validation and injection vulnerabilities
Compatibility Testing — Hardware, OS, Browser compatibility matrices
Mobile compatibility — iOS / Android version matrix
Installation Testing — fresh install, upgrade, uninstall, reinstall scenarios
Garbage testing — leftover files and registry entries after uninstall
Recovery Testing — system recovery after crash or failure scenarios
Disaster recovery testing and failover validation
Regression Testing — Unit, Regional & Full regression. Ensures new changes don't
break existing functionality
Smoke Testing — Build Verification Testing — quick sanity check that critical
functions work before deeper testing begins
Sanity Testing — narrow, focused testing after minor changes to verify specific
functionality
Exploratory & Ad-Hoc Testing — unscripted, experience-driven testing
Monkey Testing — random inputs to find unexpected failures
Severity measures the impact of a defect on the system
Priority determines the order in which defects should be fixed
A cosmetic bug on the homepage may have low severity but high priority — master
this distinction or expect arguments with developers
Globalisation Testing — ensures the application can support multiple languages
and regions
Localisation Testing — verifies correct adaptation for a specific locale — date
formats, currency, language, and cultural nuances
Equivalence Class Partitioning (ECP) — divides input data into valid and invalid
equivalence classes
Only one test case per class is needed — dramatically reduces total number of
tests while maintaining coverage
Boundary Value Analysis (BVA) — focuses on values at the boundaries of input
ranges (minimum, maximum, just inside, just outside)
Most defects occur at boundaries rather than in the middle of ranges
Combine ECP + BVA for the most efficient test set
Decision Table Testing — map conditions, actions, and rules for complex business
logic scenarios
Especially useful for business rules with many combinations
Pairing decision tables with code reviews catches logic bugs early
State Transition Testing — model states, events, and transitions using diagrams
and tables
Critical for workflow-driven applications
Error Guessing — experience-based technique that combines well with structured
techniques
STLC framework — Requirements → Planning → Test Cases → Environment → Execution
Test Plan — scope, strategy, resources, schedule
Use Case — actor-driven business scenario
Test Scenario — high-level testing goal
Test Case — specific steps + data + expected results
RTM — Requirements Traceability Matrix linking requirements to test cases
Defect / Bug Life Cycle — New → Assigned → Open → Fixed → Retest → Verified →
Closed (with Reopen and Deferred branches)
Defect report best practices — reproducible steps
Expected vs actual results
Environment details
Screenshots, logs, videos
Severity vs Priority on every report
Categorisation (functional, UI, performance, security)
Your defect reports are your interface with developers — a reproducible bug
report fixed in 1 hour beats a vague bug report rotting in the backlog for 2 weeks
Agile principles for testing teams
Scrum roles, artifacts & events from the tester's perspective
Agile Testing Quadrants (Brian Marick)
Test Pyramid in Agile — automation as a default mindset
Definition of Done including testing criteria
Burndown & Burnup charts for sprint tracking
Test estimation in story points
Mid-sprint course corrections
Sprint retrospective from a QA lens
Creating test cases & bug reports in JIRA — the GCC standard
JIRA workflows for defects
JIRA queries for QA reporting
Linking issues, requirements, and tests
Basic Git for QA engineers — clone, branch, commit, PR
Reviewing pull requests as a tester
Linking JIRA issues to commits
Interview preparation and resume building for QA roles
Common QA interview question patterns
Foundation for ISTQB Foundation Level (CTFL) certification
03
Automation Testing
From Selenium fundamentals to Playwright's modern architecture, AI-powered
self-healing tests, and full CI/CD pipeline integration — a complete automation engineering
curriculum. Five modules giving you fluency in both the industry-standard automation tool (Selenium)
and the 2026 production default (Playwright) — with AI-powered self-healing layered on top.
Speed — execute thousands of tests rapidly
Reusability — run same tests across builds
Cost-effectiveness — reduce manual effort over time
Consistency — eliminate human error
Automation Pyramid — Unit Tests (base) → Integration Tests (middle) → E2E Tests
(top)
Invest most in unit tests for fastest feedback; E2E tests are valuable but
expensive
Selenium WebDriver communicates directly with browsers via browser-specific
drivers
ChromeDriver, GeckoDriver, EdgeDriver
Language bindings — Java / Python + Eclipse / IntelliJ / VS Code
WebDriver installation & configuration
Locators — ID, Name, Class Name, Tag Name
CSS Selectors (Basic & Advanced)
XPath (Absolute & Relative)
Link Text, Partial Link Text
Prefer CSS selectors over XPath when possible — they're faster and more readable
Navigation Commands — get, navigate, refresh, back, forward
Browser Commands — maximize, minimize, fullscreen, close, quit
WebElement Commands — click, sendKeys, clear, getText, getAttribute, isDisplayed,
isEnabled
Web elements — text boxes, buttons, radio buttons, checkboxes, dropdowns,
multi-select
Implicit Wait (global)
Explicit Wait (WebDriverWait)
Fluent Wait (polling interval)
Custom Expected Conditions for dynamic elements
Never use Thread.sleep() in production tests — it's the most common source of
flakiness
Alerts & pop-ups
Multiple windows/tabs
iFrames and Shadow DOM elements
Dynamic elements & AJAX calls
Mouse actions — moveToElement, dragAndDrop, rightClick, doubleClick
Keyboard actions
JavaScriptExecutor for scrolling and hidden elements
Page Object Model (POM) — the universal standard
Page Factory for cleaner POM
Base Classes and Utility Classes (Excel, Properties, Screenshots)
Data-Driven, Keyword-Driven, Hybrid, BDD framework patterns
TestNG annotations — @Test, @BeforeMethod, @AfterMethod, @BeforeClass,
@AfterClass
testng.xml configuration
Hard vs Soft Assertions
Parameterisation and Data Providers for data-driven tests
Grouping & filtering
Parallel Execution
Listeners & Reporting
Data-driven testing with Apache POI (Excel)
CSV, JSON, XML data sources
JDBC database connectivity for data-driven tests
Auto-waiting mechanism — no flaky waits
Network interception & mocking built-in
Multi-tab & multi-context support
Built-in test runner with HTML reports
AI-powered self-healing locators
Supports Node.js, Python, Java, .NET
Playwright vs Selenium — auto-wait built-in vs manual, native vs 3rd party
network mocking, faster execution
Smart locators — getByRole, getByText, getByLabel, getByTestId
Semantic, resilient, and auto-retrying locators that reduce test brittleness
Smart locators are why Playwright tests stay green across UI refactors that would
break a Selenium suite
Codegen test generator
Playwright Inspector
Trace Viewer with detailed execution traces
Screenshots & videos on failure
PWDEBUG mode
Chromium, Firefox, WebKit — three browser engines from one API
Mobile emulation with iPhone/Android device descriptors
Responsive testing and parallel execution with sharding
For greenfield projects in 2026, start with Playwright; for maintaining existing
Selenium suites, keep Selenium — but learn both
Self-Healing Tests — Healenium & AI4Selenium automatically update broken locators
when UI changes
Dramatically reduces maintenance overhead — takes test automation from "always
broken" to "always green"
AI Test Generation — AI-powered tools generate test cases from user stories,
requirements, and application behaviour analysis
LLM prompts for test case generation
Validating AI-generated tests for quality
Visual AI Testing — Percy & Applitools provide pixel-by-pixel comparison
Intelligent visual regression testing handling dynamic content
Catches UI bugs that functional tests miss entirely
Cloud Testing — BrowserStack, Sauce Labs, LambdaTest — real device farms,
cross-browser grids, parallel execution at scale
When to use cloud vs local
Cost optimisation strategies for cloud test execution
Performance Testing in Automation — measure page load times
Integrate Lighthouse for Core Web Vitals — FCP, LCP, TTI
Apply network throttling to simulate real-world conditions
Track performance regressions across builds
In 2026, the test automation engineer who doesn't know AI-powered testing is
being out-shipped 5:1 by engineers who do
Workflow configuration (YAML)
Running tests on Push / Pull Request
Matrix strategy for multi-browser execution
Artifacts & HTML reports upload
Azure DevOps Pipelines integration
Selenium Grid with Docker
Playwright Docker images
Docker Compose for multi-container setup
Headless mode for CI execution
Page Object Model for maintainability
Consistent naming conventions
Avoid test flakiness — use proper waits, never Thread.sleep
Test data independence
Parallel execution strategies
Logging & debugging standards
Regular test maintenance & refactoring
Common challenges — StaleElementReferenceException
TimeoutException handling
Dynamic locator strategies
Test execution speed optimisation
Project — complete Selenium + Playwright automation framework, both wired into
GitHub Actions with matrix builds running tests on Chrome + Firefox + WebKit, on every push and
pull request, with HTML reports and screenshot/video capture on failure
04
API Testing
A deep dive into API testing fundamentals, Postman mastery, Python automation
with requests + Pytest, GraphQL, security testing (OWASP API Top 10), and CI/CD integration for API
test suites. Five modules that take you from "what's an HTTP method" to "I own the API test stack."
An Application Programming Interface defines how software components communicate
APIs enable integration between systems, services, and platforms
REST — stateless, resource-based, uses HTTP methods
SOAP — XML-based, strict contract via WSDL
GraphQL — query language, client-specified data
HTTP methods — GET (retrieve), POST (create), PUT (replace), PATCH (partial
update)
DELETE (remove), HEAD (headers only), OPTIONS (supported methods)
Status codes — 2xx Success, 3xx Redirect, 4xx Client Error, 5xx Server Error
Request — URL/URI structure, Headers, Query Parameters, Path Parameters, Request
Body
Response — Status Code, Headers, Response Body (JSON/XML), Cookies, Response Time
JSON essentials — Objects, Arrays, Key-Value Pairs, Nested Objects
JSONPath Expressions
JSON data types
Reading Swagger / OpenAPI specs
API contracts and understanding endpoint definitions
Workspaces, Collections & Folders
Request builder — GET, POST, PUT, PATCH, DELETE
Setting headers, query & path parameters
Response viewer — Pretty, Raw, Preview
Collection Runner for batch execution
Global, Environment, Collection & Local variables
Dynamic variables — $guid, $timestamp, $randomInt
Variable scope and precedence
Chai assertion library in JavaScript
Status code, response time, body validation
JSON property & schema validation
Header & cookie validation
Data-driven testing with CSV & JSON files
API Key, Bearer Token, Basic Auth
OAuth 1.0 & 2.0, Digest Auth, JWT
Pre-request scripts for token refresh
Mock Servers — test without a backend
Monitors — scheduled API health checks
Pre-request scripts for dynamic data generation
Newman CLI for command-line execution
requests — Python's most popular HTTP library for API automation
HTTP methods — get, post, put, patch, delete
Passing parameters, headers & request body
Response object — status_code, text, json(), headers, cookies, elapsed
Session objects for persistent connections
Authentication, file upload/download
SSL certificate verification
Writing Pytest test functions with assertions
Fixtures for setup & teardown
Parameterisation with @pytest.mark.parametrize
Test organisation & HTML reports (pytest-html)
Markers for test categorisation
Project structure & config files
Utility functions & test data management
Logging & reporting integration
Environment-specific configs for Dev, QA, Staging, Production
Framework best practice — keep configuration, test data, utilities, and test
logic in distinct layers
Authentication & Authorisation testing
SQL Injection & XSS testing
HTTPS & SSL/TLS validation
Rate limiting & API key security
OWASP API1: Broken Object Level Authorisation
OWASP API2: Broken User Authentication
OWASP API3: Excessive Data Exposure
OWASP API4: Lack of Resources and Rate Limiting
OWASP API5: Security Misconfiguration
Testing patterns for each vulnerability class
GraphQL Queries, Mutations & Subscriptions
Schema & Types, Resolvers
Fields, Arguments, Aliases, Fragments
Variables, Directives, Nested Queries
Schema validation & error handling
GraphQL-specific attack patterns
Testing strategies for microservices
Contract testing with Pact framework
Service virtualisation
Testing with Docker containers
GitHub Actions workflows for API test suites
Running tests on Push / Pull Request triggers
Environment secrets management
Azure DevOps Pipelines integration
WireMock (Java) & MockServer (Node.js)
Postman Mock Servers
Service virtualisation tools
Allure Reports & Extent Reports
Custom HTML dashboards
APM & observability tools
Positive & negative test coverage
Boundary value testing for APIs
Test data independence
Reusability & maintainability
Clear test naming conventions
Avoid hard-coded values
Environment configuration management
Swagger / OpenAPI documentation validation
API Testing interview questions
REST Assured & Postman questions
GraphQL interview questions
Coding challenges & resume building
Foundation for emerging API Testing Engineer body of knowledge
05
Generative AI & Agentic AI
The production AI engineering destination — and where this programme
distinguishes itself from every other Indian QA course. From the 70-year arc of AI history to
deploying a production Testing Coding Agent — this section builds the complete 2026 GenAI
engineering stack tuned for QA work: frontier models, prompt engineering, RAG, agent frameworks, and
the Model Context Protocol. The named Testing Coding Agent project lives here.
Narrow AI — image classifiers, speech recognition — the pre-2022 era
Generative AI — LLMs, image/video/audio generation — the post-2022 era unleashed
by ChatGPT
Agentic AI — Plan / Reason / Act / Learn loops, tool use — the post-2024 era of
autonomous systems
2022 inflection point — ChatGPT launch enters mainstream consciousness
2024 inflection point — AI systems begin planning, using tools, completing
multi-step tasks autonomously — including QA tasks
What's coming 2026-2030 — increasingly capable reasoning models, deeper tool
integration with test infrastructure
Multi-agent collaboration at scale and systems that learn continuously from
production feedback
For QA engineers, the agentic era means agents that can investigate failures,
draft remediation tests, and propose fixes — with you reviewing and approving
GPT-5.5 — The Autonomous Agent. Terminal-Bench 2.0 leader at 82.7%
GPT-5.5 best for autonomous agents and computer-use testing
Claude Opus 4.7 — The Precision Coder. SWE-bench Pro leader at 64.3%, lowest
hallucination rate at 36%
Claude Opus 4.7 — deepest native MCP support of any frontier model, critical for
test tool integration
Gemini 3.1 Pro — The Context Giant with 2M+ token context window large enough to
ingest entire test repositories
Open-source frontier — Llama 4 (Meta), DeepSeek, Mistral, Qwen — when you need to
run models in your own VPC for sensitive test data
Intelligent Routing for QA — Opus 4.7 as daily driver for test case generation
GPT-5.5 for autonomous test execution and triage agents
Gemini 3.1 Pro for ingesting massive log files and entire test codebases
GitHub Copilot in your IDE for test code generation
Copilot Studio for building custom QA agents
Perplexity — citation-grounded research for testing best practices
NotebookLM — long-document analysis for requirements docs
ChatGPT Codex — agentic test code generation environment
Fundamentals — Context + Task + Examples + Format + Constraints
Core Techniques — Zero-shot, few-shot, Chain-of-Thought (CoT), ReAct,
Tree-of-Thought
System Prompts — persistent persona design, guardrails, extended thinking
Multimodal — reading UI screenshots for visual test generation
Hallucination & Context — grounding for accurate test case generation
Domain & Library — QA-specific prompt patterns + versioned prompt library
Context Engineering — managing what enters the LLM's context window — for QA this
means feeding the right user stories, acceptance criteria, and historical test data at the right
moments
Project — ship a 30+ prompt library for QA work on GitHub (test case generation,
defect triage, test data generation, etc.)
Using ChatGPT, Claude, and Gemini for daily QA work
AI for test documentation — test plans, test cases, defect reports
Research with Perplexity for testing best practices
Microsoft Copilot in Word, Excel for QA reports
AI for code — GitHub Copilot, Cursor, Claude Code for test automation
Building QA-specific AI workflows that save 10+ hours per week
Reading UI screenshots with vision models — auto-generating test cases from
designs
Analysing error screenshots from defect reports
OCR for legacy test documentation
Image generation for QA training materials
Video — extracting reproducible steps from bug recording videos
The "I'll send you a screenshot of the bug" workflow is transformed — multimodal
LLMs now generate reproducible test steps from screenshots better than humans
Hallucination — when an LLM hallucinates a test case for a feature that doesn't
exist
Prompt injection — when an attacker poisons your AI's context
Privacy — keeping production data out of public LLMs during test generation
Security — secrets management when AI tools have access to test repos
Regulatory landscape — EU AI Act, India DPDP Act
Testing AI features — how QA validates non-deterministic AI outputs (evaluation
harnesses, golden datasets, hallucination metrics)
In 2026, QA engineers are increasingly responsible for testing AI features — the
skill of grading AI is becoming as core as the skill of grading software
Streamlit — rapid prototyping for internal QA dashboards
FastAPI — production-grade Python API for AI testing services
Building chatbots for test case lookup
Building diagnostic agents for common test failures
Build and deploy a Streamlit + FastAPI internal tool that answers QA questions
from your team's documentation
LLM APIs in production — OpenAI, Anthropic, Google GenAI, DeepSeek Python SDKs
API patterns — completions, chat, streaming, function calling, structured outputs
Rate limits, retries, exponential backoff
Cost tracking for AI API spend
Function calling & structured outputs — the 2026 production pattern for reliable
JSON
Pydantic-validated structured outputs — type-safe AI
Use cases — extracting structured data from failures, generating valid test data
Embeddings — OpenAI text-embedding-3-large, Voyage, Cohere models
Vector databases — ChromaDB (local/dev), Pinecone (managed), Qdrant
(open-source), pgvector (PostgreSQL)
Indexing strategies — HNSW, IVF
RAG pipeline — Chunk → Embed → Index → Retrieve → Augment → Generate
Building RAG over your test repositories, defect histories, and requirements docs
Hybrid search (BM25 + embeddings) for technical test documentation
Re-ranking with cross-encoders
Agentic RAG — self-improving retrieval where the agent decides if it has enough
QA context
Multi-step retrieval — first find the right test plan, then the right test case,
then the right execution history
Project — Internal QA RAG App: RAG over your team's test plans, defect
histories, and requirements docs; deployed with hybrid search and re-ranking; ChatOps integration
with Slack
LangGraph 1.0 — complex stateful workflows, graph-based state machines,
human-in-the-loop, LangSmith observability — the production default for agentic QA
Claude Agent SDK — powers Claude Code, deepest MCP integration critical for QA
tool calls, extended thinking for complex defect triage
CrewAI — role-based multi-agent crews, fastest prototyping; use case: a "QA team"
of agents (Test Designer, Test Executor, Defect Triager, Report Writer)
Semantic Kernel / Microsoft Agent Framework — enterprise .NET stacks
Pydantic AI — type-safe Python, validation-first agent design
ReAct (Reasoning + Acting) — investigate failure, then propose fix
Plan-and-Execute — generate a multi-step test remediation plan
Reflection loops — agent reviews its own generated test cases before executing
Multi-agent collaboration — Test Designer proposes, Reviewer critiques, Executor
runs
Human-in-the-loop checkpoints — humans approve every production-touching action
Production QA agents are 90% about state management, observability, and human
approval gates — the LLM is the easy part
MCP — the open standard for connecting agents to tools, data, and systems
Proposed by Anthropic in late 2024, now stewarded by the Linux Foundation
200+ server implementations and 97M+ monthly SDK downloads
Adoption across Anthropic, OpenAI, Google, Microsoft, AWS, and 50+ partners
For QA — MCP servers exist for GitHub, GitLab, Jira, Selenium Grid, Playwright,
BrowserStack, and dozens more
Build an MCP server exposing your test repositories with permission-aware
authorization
Build an MCP server exposing Jira for safe defect creation and triage
Build an MCP server exposing CI/CD pipelines for safe test execution
Connect LangGraph agents to multiple MCP servers via adapters
Use Claude Agent SDK's deepest native MCP integration
A2A Protocol — Google-led agent-to-agent communication standard with 50+ launch
partners
Linux Foundation governance
Three state management levels — session-level, agent-level, task-level
TESTING CODING AGENT CAPSTONE — multi-agent Testing Coding Agent using LangGraph
+ Claude Agent SDK with MCP servers exposing your test repositories, Jira bug tracker, and CI/CD
pipelines
Agent generates test cases from user stories, heals broken Selenium/Playwright
locators, triages flaky tests, and proposes test data
Frontend with React, backend with FastAPI, observability via LangSmith — human
approval gates for all defect creation and test commits — the named project for the entire
Testing & AI Agents programme
Tools you'll master
32+ testing & AI agent tools, one production project.
Real-time projects
You don't watch videos. You ship software.
Three full-production projects, each threaded through the entire curriculum. By the project, you've built the whole stack around them.
Hero project · weeks 3–12
Self-healing E2E test suite + auto-triage agent
Ship a production-grade testing platform that watches selector drift, heals itself, and triages failed runs with an LLM agent — all wired into your CI on day one.
01Page Object E2E suite in Playwright + TypeScript with parallel sharding, BrowserStack/Sauce Labs grid, retry policies, and visual regression via Percy.
02API contract + load suite — REST Assured / Karate contract tests, k6/Gatling load profiles wired to a perf dashboard, Postman collections in CI.
03Self-healing layer — a LangGraph agent that watches selector drift, proposes locator fixes, and opens auto-PRs with passing reruns.
04Auto-triage agent that clusters failed runs, classifies as flake / regression / env, drafts Jira tickets with stack traces, and pages the right team via MCP-served tools.
Outcome: ~80% flake reduction
Triage time: −60%
Reviewer: SDET leadership panel
PlaywrightLangGraphBrowserStackMCPPercy
Enterprise · weeks 6–11
Performance + chaos lab
Build a JMeter / Locust / k6 perf bench with Grafana dashboards, integrate Chaos Mesh experiments in K8s, and have an LLM agent draft post-incident reports with cost-of-downtime estimates.
JMeterk6Chaos MeshLangChain
Real-time · weeks 8–12
Security & accessibility test agent
Combine OWASP ZAP scans, dependency CVE checks, and axe-core a11y audits — feed findings into a LangGraph agent that prioritises by impact and writes remediation tickets in Jira.
OWASP ZAPaxe-coreLangGraphCVE
Project · weeks 11–12
Your AI testing platform in a real partner org.
Pick a real partner product. Deploy a production test platform — Playwright E2E + API + load + security suites, self-healing locators, auto-triage agent, MCP-served tools — into a partner team that's running it on real CI.
Download the real world project
Full scope, sample partner orgs, weekly milestones, and grading rubric — PDF, 14 pages.
2026: 220+ deployed76% → placement offers
Your instructor
Taught by engineers who shipped agentic AI to production.
MK
Manikanta Kona
Founder, Digital Lync · Principal QA & SDET Architect
Playwright · Selenium · REST Assured · k6 · LangGraph · MCP · CI/CD
"A 2026 SDET doesn't stop at writing Selenium tests. They run a self-healing E2E
suite, ship MCP-served tools that triage failed runs in CI, and wire contract + perf + security checks into
every pull request. That's the bar I teach to, every cohort."
15 yrs
QA & SDET ARCH
2,400+
LEARNERS
4.9 /5
RATING
Manikanta is the founder of Digital Lync and brings 15 years of enterprise QA & SDET architecture from AT&T, Salesforce, Cox Communications, and Broadcom — where he led test automation, performance engineering, and quality programmes for Fortune-500 banks, telcos, and insurers. Most recently he architected production self-healing test rigs that pair Playwright + REST Assured suites with LangGraph triage agents and an MCP tool layer the on-call SDET team actually trusts in CI.
His classes get you two things other programs don't give you: a founding architect who still ships production test platforms, and a curriculum rewritten every quarter to match what hiring managers actually ask about — including self-healing test adoption, AI-augmented test triage at scale, and contract + load + security testing pipelines wired into modern CI. M.S. in Engineering, Purdue University.
RK
Ravi Krishna
Chief Technologist, Digital Lync · Test Automation & AI Triage Lead
Playwright · Cypress · k6 · Postman · LangGraph · MCP · CI/CD
"A self-healing test suite stops being a slide when you stake an SLA on it — when
Playwright locators heal themselves, a LangGraph agent triages every failed run, and contract + perf +
security checks are the way the on-call SDET actually works on a Tuesday at 3am. AI triage in CI isn't
optional anymore. That's what I teach."
10 yrs
SDET
1,800+
LEARNERS
4.8 /5
RATING
Ravi is Chief Technologist at Digital Lync, where he leads the test automation and AI triage practice. After ~10 years building production SDET platforms across enterprise engineering teams, he stepped into the Chief Technologist seat to wire Playwright, Cypress, k6, LangGraph, and MCP into the way QA teams actually work — self-healing locators tuned to real production drift, MCP tool policies SDETs trust in CI, contract + load + security suites, and AI-augmented triage that paged the right team before the dashboard caught fire.
His test automation modules are built from real production post-mortems, not slide decks. Expect to leave with a working Playwright + REST Assured suite, a k6 perf bench, a LangGraph self-healing & auto-triage agent, an MCP server with auth + scope policy, and a Jira-integrated remediation pipeline you can stake an SLA on. Hyderabad-based, hands-on, and known for the unglamorous parts of testing that everyone else skips.
HIRING PARTNERS · INDUSTRY VOICES
What QA employers say about Digital Lync grads.
Real feedback from QA and SDET leaders at AI-first companies and the firms hiring our Testing + AI Agents graduates.
Digital Lync grads ramp 40% faster on release cycles than typical SDET hires. Best Testing + AI Agents pipeline in India.
Aakash Mehta, QA Director, Microsoft
We've onboarded 80+ Digital Lync alumni in 18 months. Lowest ramp time we've seen for self-healing test suites and auto-triage practices.
Anita Sharma, Senior Manager, Deloitte
The Testing + AI Agents programme is comprehensive — Playwright, k6, OWASP ZAP, AI triage. Grads come pre-trained for production self-healing test engineering.
Rahul Bhatt, Solutions Lead, Mphasis
Their self-healing tests + auto-triage track produces PMs who ship production-grade test suites on day one. Rare combination of test rigor and SDET craft.
Deepak Pillai, Senior Architect, TCS
What sets Digital Lync apart is the AI agent layer baked into the SDET track. Our enterprise clients ask for exactly this profile.
Suresh Menon, Practice Lead, Accenture
Their ISTQB + CSTE prep is rigorous, and the shipped project — self-healing E2E suite, auto-triage agent, perf bench — is what closes interviews for us.
Vikram Iyer, Director, Infosys
Digital Lync's Test engineers ship self-healing test suites twice as fast in the first 90 days. Our internal flake-rate metrics back this up clearly.
Lakshmi Nair, VP Engineering, Wipro
Best Testing + AI Agents pipeline we've sourced from in India. Their projects are real shipped test rigs, not happy-path demos.
Karthik Subramanian, Engineering Director, Cognizant
Strong Playwright and SDET engineering foundation. Their Testing grads need almost zero ramp time on enterprise QA engagements with us.
Arun Joshi, Practice Director, Capgemini
We've placed 40+ Digital Lync alumni across our QA and watsonx test teams. Strong fundamentals, sharp on eval and CI.
Sanjay Verma, Talent Director, IBM
self-healing tests + auto-triage is exactly the talent gap we've been struggling to close. Digital Lync is filling it for us reliably.
Anjali Desai, Practice Head, LTIMindtree
Their Testing track delivers SDETs who navigate Playwright, k6, and CI on customer engagements unsupervised.
Ramesh Iyer, Senior Manager, Tech Mahindra
Hired 25+ Digital Lync graduates for our QA engineering practice. Strong on Playwright, sharp on perf testing, fluent in AI triage.
Geetha Pillai, Talent Acquisition Lead, Cyient
Digital Lync grads who blend self-healing tests with Azure OpenAI triage land production-ready on day one. Rare combination, well-trained.
Priya Reddy, Talent Lead, Microsoft
03Program certifications
An Agent‑Ready credential, not a participation trophy.
Digital Lync · Institute Certificate
Agent‑Ready Test Engineer
Presented to
Spandana Bala
For the successful design, build, and production deployment of a self-healing test platform —
Playwright E2E suite, API contract + load + security suites, self-healing locator agent, and auto-triage agent — evaluated against the ISTQB Advanced, CSTE, and AWS Cloud Practitioner credential rubrics.
Manikanta Kona
CEO · Digital Lync
AGENT
READY
2026
READY
2026
01
Industry‑recognized
Co‑branded with the QA engineering community and mapped to ISTQB Advanced
and CSTE credentials — names that hiring managers already scan for on resumes.
02
Project artifact included
Every certificate carries your shipped project — Playwright E2E suite, self-healing locator agent, auto-triage agent, perf bench — with
a link to the live partner-org deployment. Proof, not a promise.
03
Enhanced skill validation
Graded against the 2026 Agent‑Ready rubric: E2E suites, contract testing, perf & chaos labs, security & a11y testing,
self-healing & AI triage. No pass/fail — a level 1‑5 band.
04
Verifiable on a public URL
Each credential has a public verification page recruiters can check in 10 seconds — no PDF
back‑and‑forth.
04Job placement support
Your first SDET offer isn't a lottery ticket. It's a built process.
GitHub, LinkedIn, resume — and most importantly, warm intros into QA-mature SaaS and product teams. Our
placement team works your search like an account, not a helpdesk.
01 / GITHUB & PORTFOLIO
A portfolio, not a graveyard.
Guidance on building a portfolio that showcases your Playwright E2E suite, self-healing locator agent, auto-triage dashboard, perf bench, and a public verification URL — reviewed 1:1, not via template.
02 / RESUME PREP
Rewrite, don't proofread.
A one-page resume rebuilt around the test platforms you shipped (Playwright suites, self-healing agents, auto-triage), the partner-org project, and the business outcome. Reviewed by SDET leaders who've read 10,000+ resumes.
03 / LINKEDIN + INTROS
Where most opportunities actually live.
Profile tuning plus direct warm introductions into QA-mature SaaS and product teams — Microsoft, BrowserStack, Sauce Labs, Atlassian, Postman, Datadog, Anthropic, Hugging Face, Stripe, Razorpay, Freshworks, Zoho, plus services that staff QA practices (Deloitte, Accenture, Cognizant, TCS). You leave with recruiter contacts, not a generic "good luck."
Testing alumni
Hundreds of SDET careers launched — here are eight.
NV
Naveen Vedala
Senior Test Automation Engineer
Hyderabad · India
Now at · Atlassian
TA
Tejashwini Addla
Staff SDET (Self-healing)
Hyderabad · India
Now at · Salesforce
TD
Tharunesh Dillikar
Principal Test Engineer
Seattle · United States
Now at · Sauce Labs
MM
Mujahed Mohammed
Performance Engineer
Hyderabad · India
Now at · Databricks
BK
Bhargav Kumar Murala
QA Architect
Hyderabad · India
Now at · Adobe
SL
Sai Manasa Leburi
AI Triage Engineer
New York · United States
Now at · Hugging Face
RD
Rahul Dhamma
Director of QA
Hyderabad · India
Now at · BrowserStack
Our locations
Come chat with us — over coffee, or over Zoom.
One flagship campus in Hyderabad, plus online Principal Test Engineer cohorts running on Indian and US timezones.
Flagship campus
Hyderabad
2nd Floor, Hitech City Road · Above Domino's · Opp. Cyber Towers, Jai Hind Enclave ·
Hyderabad, Telangana
Call
+91 90003 29956
US desk
+1 858 666 6719
Hours
Mon–Sat · 9am–9pm
Online class
Global
Weekend and evening Testing cohorts running on IST and PST. Every online cohort ships
the same shipped project — Playwright E2E suite, self-healing agent, auto-triage, perf bench — as the on‑campus track.
Timezones
IST & PST
Format
Live + 1:1 mentorship
Next class
25 May 2026
FAQ
Questions we actually get — answered honestly.
Straight answers on prerequisites, the testing stack, certifications, and placement. If something's missing, book a 20-minute advisor call — no slides, no pitch.
Do I need a CS background or prior testing experience?
No on both counts. Roughly 40% of every class comes from non-CS streams — mechanical, electrical, BCom, BBA, manual testers, and self-taught coders. Weeks 1–2 cover the JavaScript/Python fundamentals, web protocols, and Git basics from scratch. What you do need: consistency and 12–15 hours a week.
Will I actually ship test suites to CI, or only do exercises?
You actually ship. Every learner builds a Playwright E2E suite running on BrowserStack/Sauce Labs, a k6/JMeter perf bench, OWASP ZAP security scans, a LangGraph self-healing locator agent, and an auto-triage agent that opens Jira tickets. The project runs on a partner CI — not a homework repo.
Which testing tools and AI models will I use?
E2E: Playwright, Cypress, Selenium, WebdriverIO, Appium. API: REST Assured, Postman, Karate. Unit: pytest, JUnit, TestNG, Jasmine, Mocha. Perf: JMeter, Locust, k6, Gatling. Cloud grids: BrowserStack, Sauce Labs, Percy. Security: OWASP ZAP. AI: OpenAI, LangChain, LangGraph, MCP.
Will I prep for AIPMM SDET and Pragmatic Principal Test Engineer certs?
Yes. The curriculum is mapped to the AIPMM SDET track and the Pragmatic Principal Test Engineer credential. We run two full mock exams and reimburse the voucher fee on first-attempt pass.
What's the time commitment per week?
Plan for 12–15 hours: 2 live classes × 2 hours, 1 lab × 3 hours running suites in your training CI, and ~5 hours of project work (Playwright, perf, AI triage). Saturday office hours with the TA team are optional, but most learners use them.
Is placement support really 1:1, and which companies hire SDETs?
Yes — a dedicated placement advisor from week 8, not a helpdesk. AI product hiring partners include Microsoft, Adobe, Salesforce, Atlassian, Notion, Linear, Anthropic, Hugging Face, Databricks, Snowflake, Stripe, Razorpay, Freshworks, Zoho, and Postman. Resume, LinkedIn, mock interviews, and warm intros are individual.
Online, weekend, or on-campus?
All three. On-campus at the Hyderabad flagship, live online (IST and PST cohorts), and a weekend track for working professionals. Every format ships the same shipped project — Playwright E2E suite, self-healing agent, auto-triage, perf bench — only the schedule changes.
What if I fall behind, or can't continue mid-class?
Freeze your seat for up to 90 days and rejoin the next class — no extra fee. TAs run catch-up sessions every Saturday for anyone more than a week behind, and recordings of every live session are available for the lifetime of your account.
Still have a question? Talk to an advisor — no slides, no pitch.
Class TES-029 starts 1 Jun 2026.
40 seats. 12 already claimed.
Book a 20-minute advisor call. We'll walk through the curriculum, match it to your current role, and show you two real projects from class 022.
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