Level Up Software Testing Using Generative AI

A prompt is the message you send to ChatGPT to specify what you want to know or discuss, with prompt creation becoming a nuanced, refining process.

Craft clear, specific prompts for software testing with generative AI by defining objectives, context, and style, using step-by-step breakdowns and constraints to drive precise QA outputs.

Explore multiple output formats for software testing prompts, from plain text to tables, nested lists, bullet points, markdown, CSV, and emojis, and apply them to real world testing scenarios.

Generate JSON, XML, YAML, SQL queries, regex patterns, and shell commands with ChatGPT to streamline test automation and enable data-driven testing, API validation, and configurable reports.

Learn strategies to reduce AI hallucinations in software testing, verify factual accuracy with step-by-step reasoning, and request sources for credible validation of test results.

Use the expert persona pattern to guide AI responses by acting as specific roles, shaping tailored, role-based testing guidance from QA leads to interviewers and product managers.

Leverage ai to generate state transition diagrams that visualize a system's behavior for testing a pos checkout flow using mermaid code and automated prompts.

Create a mind map of your testing strategy in minutes using AI, outlining main topics, subtopics, and actions for thorough testing of a POS app.

Analyze test scope and generate test cases for a POS system using AI, covering functional, usability, and performance testing along with authentication scenarios and tabular export for test management.

Harness ai tools like ChatGPT to generate, customize, and explain jpql queries for Jira filters, enabling fast searches for in-progress issues, last-month bugs, and due dates by month end.

install and run local language models with Olama to keep data private and offline. learn Mac setup, model commands, and how Olama serves a local llm ecosystem with various models.

Learn to run Python programs that communicate with Llama 3 through Ollama, enabling local test automation such as generating boundary value test cases and summarizing test logs.

Open Web UI runs local language models securely offline with Docker, manages models, enables retrieval augmented generation from local documents and web, and generates images with automatic 1111 or Dall-E.

Discover how to customize models in Open Web UI by creating model files with name, description, and prompts for QA testing, then use prompts, themes, and local or OpenAI models.

Enable secure remote access to your self-hosted open web UI with ngrok, establishing a secure tunnel and a public URL to access your AI playground from any device.

Level up software testing with generative AI by using hugging face's open source tools, including transformers, datasets, and the hub for NLP models and data.

Master key terms for using Hugging Face in software testing with generative AI, including a pretrained model, training, inference, transformers, tokenizer, and tokens for text tasks.

Learn how to set up a Hugging Face account and environment, install transformers and diffusers, run sentiment analysis with a pre-trained Distilbert model, and explore models and spaces for integration.

Discover how an AI agent—comprising a language model, tools, and an orchestration layer—acts on your behalf to automate testing tasks with intent, generating structured JSON outputs.

Explore GitHub Copilot's agent mode, a real-time in-editor pair programmer that tests and fixes code. An asynchronous cloud teammate, the coding agent edits code, runs tests, and opens pull requests.

Level up test automation with Copilot agent mode and Playwright MCP server to access external tools within VSCode for end-to-end workflows.