CHATGPT for Software Testing | Generative AI

Explore how neural networks mimic brain connections with input, hidden, and output layers, using weights and transformations to learn patterns via supervised learning and deep learning when layers exceed three.

Set the temperature near zero for consistent outputs, and with data controls disable chat history to prevent prompts from training and sharing across instances for reliable test automation using given-when-then.

Learn how generative AI models hallucinate due to statistical token prediction, with real-world examples, and how to minimize hallucinations by lowering temperature and relying on training data.

Examine privacy concerns in OpenAI and how user inputs in ChatGPT 3.5/4 may be used for training, underscoring enterprise controls and avoiding sensitive data.

Explore openai's models GPT four, zero one preview, and A01 mini, pricing and uses, and how the GPT plus subscription offers priority access to Dall-E, file upload, and code interpreter.

Use the code interpreter to generate Python code, analyze Kaggle real estate data, plot visuals, and export a downloadable one-page report for defect tracking with JIRA.

Explore the OpenAI GPT store, a marketplace of GPTs by individuals and companies, and learn to import and reference them in your conversations with custom inputs.

Explore platform.openai.com to access tutorials, model docs, prompts, and APIs; learn to create assistants, fine-tune models, and implement moderation, SDKs, and usage guidelines.

Learn what a token is in a large language model as a unit of text that can be a word, letter, or space, using a tokenizer tool.

Explore OpenAI pricing from settings billing, including per-1000-token costs for turbo (input $0.01, output $0.03) and GPT-4 versus GPT-3.5, image pricing, usage history, and spending limits.

Obtain and securely manage your API key by creating a new secret, naming it, and using the generated key for all demos and all get or post requests.

Explore the full set of OpenAI API endpoints, including text-to-speech, transcription, translation, completion, embeddings, fine-tuning, image generation with DALL-E models, and content moderation.

Learn to make your first chatgpt api request with curl by setting the api key, choosing the gpt-3.5 model, and adjusting the temperature, while understanding token counts.

Discover AI assisted code coverage, or self-healing code, by training a model on your code and coverage reports to generate missing unit tests and reinforce learning through feedback.

Explore a GitHub CI workflow where AI fixes code and writes unit tests to raise code coverage above thresholds, triggering repeated commits until checks pass, with human review for quality.

Explore a next generation testing framework that uses analytics to drive continuous acceptance testing and production monitoring, via given–when–then scenarios fed by user behavior and model insights to prioritize usage.

Train your AI with UI details, mocks, and existing test scripts to generate valid Selenium test cases, aligned with your page object model and company best practices.

Learn to generate a high level ERP test strategy with ChatGPT using basic, intermediate, and advanced prompts, zero-one preview, canvas editing, and comprehensive risk-based cross-module testing for automation and performance.

Use ChatGPT to design a robust test framework for a React front end and NodeJS backend hosted on Azure, detailing tool recommendations, team composition, and reasoning.

Learn how to generate user acceptance tests and acceptance test templates with ChatGPT, crafting given-when-then scenarios from a user story, covering functional and nonfunctional requirements.

Explore the Blaze demo.com app by Blazemeter to learn front-end automation, element inspection, and identifying selectors (tag, id, CSS, XPath) using the Playwright framework.

Install and configure Visual Studio Code as your code editor, install Java and Maven, and set up Java debugger along with GitHub actions for continuous testing and Java-based test automation.

Install Node.js and npm on Windows, pick a code editor, set up your Playwright project, install Playwright with npm, and verify the setup to prepare for automated testing.

Run npm install playwright, set up a working folder, install browsers with npx playwright install, and run tests with npx playwright test to capture a screenshot.

Identify the most critical tests for a plane ticketing app using chatgpt, prioritizing home page accessibility, flight search with invalid data, booking flow, session management, and test-case driven automation.

Debug manually a Playwright front-end test by inspecting elements and adjusting selectors, then fix assertion strings and discuss refactoring to decouple data with a page object model.

Add logs and capture screenshots after each test step to simplify debugging and root-cause analysis within the playwright automation framework.

OpenAI Codex, powered by a model called Codex one derived from the O3 model, enables coding from your GitHub repo, allows asking about code, and runs and installs dependencies.

Configure the agents md file for ai-driven test automation with playwright and a page object model, using repository workflows. Include config.json settings, installation steps, and pull request guidelines.

Fix a poorly written agent.md for OpenAI Codex by defining agent instructions, config, tests, and data so tests run only when instructed.

Explore using OpenAI Codex to generate valid and invalid test data, expanding data sets under /data with multiple files, while managing environment tasks, commits, and pull requests.

OpenAI Codex generates tests with invalid data to simulate failures, using negative cases like invalid passenger info and flights data, while guiding environment setup and demo invalid.js usage.

Generate cucumber feature files to automate test coverage using Gherkin-inspired scenarios, feature files, and Cucumber Java, while defining keywords and aligning with your system's syntax.

Transform a generated cucumber feature file into a compatible format for your test system, copy the code, and refine it into a workable, project-context implementation.

Learn to generate automated tests with ChatGPT by drafting Java-based WebDriver tests, mapping elements with a page object model, and validating scenarios from cucumber feature files.

Leverage a page object model to identify locators and rewrite your Java test with GPT for project context automated testing, including the login method with username and password.

Reverse engineer Selenium WebDriver Java tests into Cucumber feature files to produce human-readable scenarios for management and audits, using ChatGPT to bridge code and Cucumber.

Test the api by crafting a json payload for a calculator exposed on port 80 localhost, calling post methods for addition, subtraction, multiply, divide, and invalid operation, and validate results.

Use ChatGPT to configure Postman by creating a class collection of operations, validate responses with tests, ensure Http 200, and check simple math outcomes like 2+3=5.

Learn to run a Postman collection from the CLI on Windows using Newman after installing Node.js and npm, enabling CI/CD test runs after pull requests.

Generate sql queries against a three-table database (customers, products, orders) using ChatGPT. Build selects, inner joins, inserts, and transaction-based scripts with realistic test data.

Generate test data for a database schema with three Excel/CSV files (customers, products, orders), then create SQL inserts or a PowerShell script to create and import the data.

Learn to run Postman collections inside a Docker container with a Dockerfile, Newman CLI, and environment variables, capture test results, and ensure portability across environments.

Leverage generative AI to add a Postman test job to a GitHub Actions CI/CD pipeline, running on Ubuntu, installing NodeJS and Newman, and setting up Postman API key after deploy.

Explore AI agents as orchestrators coordinating large language models, tools, and databases to automate tasks and enable scalable software development with prompt chaining and feedback loops.

Demonstrates an ai agent built with flow wise that automatically generates acceptance tests, bdd scenarios, and selenium test cases from a local story file.

Build an ai agent using prompt chaining with a supervisor orchestrator and worker nodes. Create a children's book and catchy titles by chaining prompts and orchestrating the workflow.