QA & testing glossary
Plain English definitions for the software testing and AI-powered QA terms you'll run into while working with Klarent.
A
AI hallucination
AI hallucination is when a language model generates output that sounds confident and plausible but isn't grounded in fact, in its training data, or in the context it was given. In AI-driven testing, that might mean a generated test case references a UI element or a field that doesn't actually exist.
AI test analytics
AI test analytics is the use of models to interpret the data a test suite produces, rather than just charting it. It clusters related failures, separates real defects from flaky ones, tracks where a suite is losing reliability, and turns thousands of run records into a short list of things worth acting on.
AI test automation
AI test automation is the practice of using artificial intelligence, including machine learning and generative models, to design, run, and maintain software tests with little or no manual scripting. AI agents interpret how an application behaves, turn plain-language descriptions into working test cases, and adjust those tests on their own as the software underneath them changes.
AI test execution
AI test execution is the run phase of an AI-driven test suite, where agents carry out test steps against a live application, decide what to do when the screen does not match expectations, and judge whether the result counts as a pass or a failure. It replaces a fixed replay of scripted commands with a run that can reason mid-flight.
AI test maintenance
AI test maintenance is the use of AI to keep an automated test suite working as the application under test changes. Instead of an engineer editing broken scripts after every release, models detect what moved in the interface or API, repair the affected steps, and flag the failures that point at real defects.
AI test prioritization
AI test prioritization is the use of models to decide which tests to run first, or at all, for a given change. It ranks a suite by how likely each test is to catch a defect in the code that just moved, so the highest-value cases run early and a pull request gets a useful signal in minutes instead of hours.
AI workflow automation
AI workflow automation is the use of AI models and agents to carry out a multi-step process end to end, deciding what to do next based on the result of the previous step rather than following a fixed script. It differs from traditional automation by adapting its path when a step's outcome isn't what was expected.
Azure DevOps
Azure DevOps is Microsoft's suite of services for managing the software development lifecycle, spanning planning, source control, builds, testing, and releases. It bundles five services, Azure Boards, Repos, Pipelines, Test Plans, and Artifacts, that teams can adopt together or individually, in the cloud or self-hosted on-premises.
C
CI/CD pipeline
A CI/CD pipeline is the automated sequence of steps that takes a code change from commit to a tested, deployable build, and often all the way to production. It typically runs builds, tests, and deployments in order, so a change earns its way to release instead of shipping on trust alone.
Cypress
Cypress is an open-source, JavaScript-based testing framework for web applications, used mainly for end-to-end and component tests. Unlike tools that drive the browser from the outside, Cypress runs inside the browser alongside the application, which gives it direct access to the DOM and a fast, visual debugging experience that front-end teams tend to favor.
P
Page object model
The page object model is a design pattern for browser test automation that wraps each page, or a distinct part of one, in its own class holding that page's elements and available actions. Tests call those methods instead of touching selectors directly, so a UI change means updating one class, not every test that used it.
Playwright
Playwright is an open-source framework for end-to-end web testing and browser automation, created by Microsoft. It drives Chromium, Firefox, and WebKit through a single API, so one test suite can check an application across the engines behind Chrome, Edge, Firefox, and Safari, written in JavaScript, TypeScript, Python, Java, or C#.
Prompt engineering
Prompt engineering is the practice of writing and refining the instructions given to an AI model so it produces a specific, reliable output. In AI-driven QA, that means shaping the wording, examples, and constraints in a prompt so a model generates accurate test cases, automation scripts, or test data instead of vague or invented ones.
Puppeteer
Puppeteer is an open-source Node.js library that gives developers a high-level API to control Chrome, Chromium, and Firefox. Maintained by Google's Chrome DevTools team, it automates browser actions for testing, web scraping, screenshots, and PDF generation, running headless by default so it fits neatly into scripts and pipelines.
S
Selenium
Selenium is an open-source suite for automating web browsers, used mainly to write and run functional and regression tests for web applications. It drives a real browser the way a user would, clicking, typing, and navigating, and lets teams script those actions in Java, Python, C#, JavaScript, or Ruby against Chrome, Firefox, Edge, and Safari.
Smoke testing
Smoke testing is a quick pass through an application's most critical functions, run right after a new build, to confirm it's stable enough to test further. It exists to catch a build that's badly broken before a team spends hours running a full test suite against something that was never going to pass.
Software testing
Software testing is the process of running a program, or examining its code, to find defects and confirm it behaves the way it's supposed to before users ever see it. It ranges from checking a single function in isolation to verifying how an entire system holds up under real usage, and it happens throughout development, not only at the end.