DEV Community: Kailash Pathak

ShapeMyInterview: The Missing Piece in QA Interview Preparation

Kailash Pathak — Wed, 01 Jul 2026 11:10:19 +0000

Why solving interview questions is not enough anymore — and how AI is changing the game for QA and SDET professionals.

As QA Engineers, Automation Testers, and SDETs, most of us prepare for interviews the same way: searching for the “Top 100 Selenium Questions,” revising Playwright concepts, brushing up on API testing, SQL joins, and CI/CD pipelines.

Yet many talented engineers still struggle during actual interviews.

The reason is simple:

Knowing the answer and communicating it under pressure are two completely different skills.

The Problem with Traditional Interview Preparation
Most interview preparation platforms focus on:

Question banks
Video tutorials
Coding challenges
Static mock tests While these resources are useful, they don’t simulate the pressure of a real interview.

A real interviewer:

Asks follow-up questions
Challenges your assumptions
Evaluates communication skills
Judges your confidence and clarity
Changes direction based on your answers

Research shows candidates often feel unprepared because they rarely practice in authentic interview environments, even when they have strong technical skills.

This is exactly the gap ShapeMyInterview attempts to solve.

Recently, I explored ShapeMyInterview, an AI-powered interview preparation platform built specifically for QA and SDET professionals, and it addresses a problem that has existed in our industry for years. According to the platform, its goal is to help candidates stop “freezing in interviews” through realistic practice, mock rounds, and live AI interviewers

Three Different Preparation Modes
One of the most interesting aspects of the platform is its structured approach to interview preparation.

1. Practice Mode
The Practice section focuses on hands-on technical challenges.

From the screenshots and platform walkthrough:

Selenium
Playwright
SQL
Performance Testing (k6)
Coding exercises
Scenario-based questions Instead of simply reading answers, candidates are expected to solve real problems and receive AI-driven feedback.

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This aligns closely with what senior QA and SDET interviews demand today.

2. Mock Interview Mode
The Mock Interview feature simulates complete interview rounds.

Key highlights include:

Timed assessments
Multi-question interview flows
Scorecards
Gap analysis
Personalized improvement plans What I particularly liked is that the platform positions itself for multiple experience levels — from freshers to architects. This makes it relevant not only for beginners but also for senior automation engineers preparing for leadership roles.

3. Live AI Interviewer
This is where the platform becomes truly interesting.

The screenshots show AI interviewers capable of:

Real-time voice conversations
Listening to answers
Asking follow-up questions
Providing evaluations
Rather than presenting a static questionnaire, the AI behaves more like a human interviewer.

Recent research on AI-driven mock interviews suggests that conversational AI can significantly improve candidate confidence and preparedness when it closely mimics real interview behavior.

For QA professionals, this is particularly valuable because communication often matters as much as technical knowledge.

Final Thoughts
The testing community has long had excellent resources for learning automation frameworks, but very few tools focused on interview readiness.

ShapeMyInterview fills that gap by combining:

Technical practice
Mock interview rounds
Live AI interviewers
JD-based preparation plans
QA-specific learning paths

For QA Engineers and SDETs preparing for their next opportunity, the platform offers something many candidates lack:

A safe environment to fail, learn, improve, and build confidence before the real interview begins.

Amazing tool

Kailash Pathak — Mon, 29 Jun 2026 09:44:32 +0000

Kailash Pathak

Jun 27

Your Test Cases Aren’t the Problem — Your Coverage Strategy Is

#ai #programming #mobile #testing

6 min read

Your Test Cases Aren’t the Problem — Your Coverage Strategy Is

Kailash Pathak — Sat, 27 Jun 2026 11:37:00 +0000

Release velocity is accelerating but most mobile teams continue to suffer from low automation coverage. Having beautifully documented test cases but still seeing the gap between test intent and execution continuing to widen resulting in painful tradeoffs between speed, quality, and risk that QA teams are forced to make. This is modern mobile development's silent crisis.

Test Your Mobile App on QApilot
Book Demo Here https://qapilot.io/book-demo

While automation has improved, traditional approaches are falling short in the face of constant UI updates, dynamic content, complex user journeys, and device fragmentation. Teams are left managing brittle scripts, high maintenance costs, flaky tests, and spending significant effort translating business scenarios into executable automation. As a result, valuable test assets remain underutilized and automation coverage remains low.

In other words, the issue isn't the quality of the test cases, it's the coverage strategy.

QApilot addresses this challenge through three test authoring modes, each addressing a different automation need. In this article we dive deep into CoWork - QApilot's Agentic Human-in-the-Loop authoring mode. You will learn how CoWork allows teams to convert existing natural-language and BDD test cases into automated test recordings, and helps in increasing automation coverage while keeping users in control throughout the process.

QApilot three test authoring modes
QApilot comes with three authoring modes depending on your needs - fully automatic test generation, human-in-the-loop AI-assisted mode, and click-by-click manual recording.

Understanding QApilot CoWork
CoWork was built specifically with QA groups in mind who have significant test documentation they want to automate effectively. CoWork analyzes the live application screen, determines the next action to take and automatically carries it out. If the UI changes or behaves unexpectedly, CoWork adjusts and replans accordingly. This hybrid method provides the smarts of AI without removing QA from the driver's seat.

What sets CoWork apart from other products that accept English or BDD as input is its integration with QApilot's knowledge graph and live agentic intelligence. Other AI testing products create scripts/nested automation from natural language input but CoWork does something much more powerful. As it runs your test cases against the application on a real device, it's constructing and utilizing a knowledge Graph of the application, and helps in increasing automation coverage across user journeys.

Knowledge Graph contains screens, UI elements, transitions, behavioral context it's turning your test execution into a knowledge base that can power other testing activities. This allows the AI planner to understand the app contextually, make decisions based on that knowledge, and most importantly, intelligently replan if it encounters UI shifts, dynamic elements, pop-ups, unknowns, or any unexpected state.

Whats Make CoWork Unique - CoWork Architecture
What sets CoWork apart from other record-and-playback tools or completely autonomous AI solutions is its stateful, context-aware architecture. While many tools can interpret natural language or BDD test cases, they typically rely on isolated, stateless execution or basic locator-based self-healing.

In contrast, QApilot's system works as follows:

The Crawler first maps the application and builds a rich Knowledge Graph - a structured model containing screens, UI elements, transitions, and contextual understanding of app behavior.
This Knowledge Graph serves as shared intelligence across the platform. When CoWork executes your BDD test cases.
Planner/Replanner leverages this graph along with real-time screen analysis, stateful memory (plan state, done/remaining steps, replan history, reasoning), and a powerful Toolbox of actions (click, scroll, type, wait, assert, etc.) to create and dynamically adapt execution plans.
The Executor converts high-level plans into actions using the Toolbox and validates outcomes through assertions and verification steps.
The system doesn't just follow steps — it understands context, makes intelligent decisions, and automatically replans when it encounters UI changes, dynamic elements, pop-ups, or unexpected states.
In the following sections, you will learn how to configure CoWork and create and execute test cases using it.

End-to-End Test Case Creation and Execution with CoWork
CoWork brings a hybrid, smart solution for test automation. Made for enterprise teams who already have test cases, but want to automate them quickly and accurately without manual scripting all the way or coding from scratch.

CoWork follows a clear, structured below six-step process:

Ingest – Create Your Test Case Library Click on CoWork from in the left sidebar. There are two tabs here, Configuration and Test Cases. The Test Cases tab will show you the CoWork Test Case Repository. Upload test cases in batches using the Excel template or enter them manually. Required fields are Allure ID, name, feature, scenario and steps. Open Test case section from the left panel.

Upload Test Case Sheet to add Cases and Steps into the platform -

We can also write test cases manually through Test Case Create -

2.Invoke — Launch CoWork

Choose which Android app source, device farm, scenario and test case to execute. CoWork will find and connect to a real device then launch into workspace mode.

*Configuration Screen *— In the below screenshot you can see there are TWO steps to select the platform and configure the scenario and test case.

Test Cases Screen -

*Launch CoWork post Configuration *— After clicking on Launch, CoWork will grab a device from your device farm and connect you live

3.Translate — Review and Edit Generated BDD Steps

CoWork transforms your natural language into editable BDD-formatted steps. We can add value to steps with Additional App Context (i.e. credentials, hints,test data etc) to train your planner for better accuracy.
The CoWork workspace is split into three panels

In the below screenshot you can see all three panels -

Left Panel — Live Device

Real-time mirror of mobile device view where CoWork executes and displays actions on the launched application.

Center Panel — Planner Workspace

Planner Workspace Interactive workspace for reviewing source requirements, editing BDD steps, adding app context, and starting automated or manual execution.

Right Panel — Planner Output

Live execution view showing plans, replans, and step status with actions, selectors, and real-time progress updates.

Execute — Knowledge Graph Powered Agentic Automation

Your AI planner observes the live screen, consults your ever-growing Knowledge Graph, chooses the best action to take, executes, then automatically re-plans.
You can watch your test execute on the live device screen being mirrored and follow along with live planner output.

Review & Accept

Review recorded steps, screenshots, and pass/fail results. Accept automation, tweak it or switch to manual edit mode.
Once execution completes, the plan steps are displayed with their final status. Review the run and choose how to proceed:
We can Click on “Accept Steps” (refer below screenshot) to save all generated steps to the test case on the platform.

Run & Report

View report summaries, failure charts, test counts, and step details from QApilot’s dashboard for executed test cases and runs in one place.
Navigate to view details on any specific reports. QApilot Reports Dashboard is the control panel that provides a comprehensive overview and detailed analysis of all executed test cases.

Report dashboard

The Test Case section in QApilot’s Reports provides a detailed, step-by-step breakdown of how each test case was executed.

Conclusion
CoWork https://qapilot.io/product/cowork combines requirement-driven automation, smart planning, and live execution to offer a more robust solution for mobile testing. Capable of automatically generating BDD steps from source requirements, execution plans, and dynamic replanning as the application under test changes behavior, all the way down to execution.

CoWork’s another powerful distinguishing factor is its Knowledge Graph, constructed by crawling the application simultaneously while taking screenshots and saving UI elements, transitions and relationships between elements and context around them. This context-aware understanding allows planners to reason better about the choices it makes, enables it to recover from changes and make execution more reliable.

You can book a demo Book a demo — https://qapilot.io/book-demo

Announcing My New Book: Web Automation with Playwright and Python using AI and MCP

Kailash Pathak — Sun, 31 May 2026 03:55:16 +0000

I'm excited to share that my second book, "Web Automation with Playwright and Python using AI and MCP", is now available on Amazon worldwide.

📖 Available on Amazon:
https://www.amazon.in/dp/9378543170

Over the last few years, I've had the opportunity to work extensively with Playwright, Python, and modern test automation frameworks. During this journey, one thing became clear: automation is evolving rapidly. We are moving beyond traditional script-based testing into a world where AI-powered agents can assist with planning, generating, executing, and even healing automated tests.
This book was written to help QA engineers, SDETs, developers, and automation enthusiasts understand and adopt these modern testing practices.

What You'll Learn
The book covers practical, real-world examples of:
End-to-End Test Automation with Playwright and Python
API Testing
Visual Testing
Accessibility Testing
Page Object Model (POM) Design Pattern
Test Framework Architecture and Best Practices
AI-Powered Test Automation
Playwright MCP (Model Context Protocol)
Intelligent AI Agents for Automation Workflows
In addition to the fundamentals, the book explores how AI and MCP can transform automation from a collection of scripts into intelligent workflows that understand intent, adapt to changes, and assist testers throughout the automation lifecycle.

Why I Wrote This Book
As automation engineers, we often spend more time maintaining tests than creating them. UI changes, flaky locators, and frequent application updates can quickly turn a stable suite into a maintenance burden.
With the emergence of AI-powered tooling and Playwright MCP, we now have an opportunity to rethink how automation is built and maintained. My goal was to create a practical guide that bridges traditional automation techniques with the next generation of AI-assisted testing.

A Thank You to the Testing Community
This achievement would not have been possible without the support of the testing community. Every discussion, conference session, blog post, meetup, and community interaction has contributed to my learning journey.
I would also like to extend my sincere thanks to 3Pillar for fostering a culture of continuous learning, innovation, and knowledge sharing.

Available Now
If you're interested in Playwright, Python, AI-powered testing, or the future of automation engineering, I hope this book will be a valuable resource for your learning journey.
📖 Available on Amazon:
https://www.amazon.in/dp/9378543170

Thank you for your continued support. I look forward to hearing your feedback and learning from your experiences with the book.

Playwright #Python #TestAutomation #QualityEngineering #AI #MCP #SoftwareTesting #SDET #AutomationTesting #AgenticAI