Software Testing & AI Glossary
The Appsierra Knowledge Library defines the QA, software testing, and AI quality terms engineering leaders search for — each with a clear, answer-first definition, a deeper explanation, and links to the services that put the concept to work. Use it to get up to speed fast and to brief your team.
QA & Testing
What is Automation Testing?
Automation testing uses software tools and scripts to run tests automatically, compare results, and report defects faster and more reliably than manual testing.
Read definition →What is Regression Testing?
Regression testing re-runs existing tests after code changes to confirm new updates haven't broken working features. Learn types, when to run it, and best practices.
Read definition →What is API Testing?
API testing validates application interfaces at the message layer for functionality, reliability, and security. Learn how it works and why it matters.
Read definition →What is Performance Testing?
Performance testing measures how an app behaves under load, checking speed, scalability, and stability. Learn the types, key metrics, and tools.
Read definition →What is Functional Testing?
Functional testing verifies software behaves to its requirements, checking each feature's inputs and outputs. Learn how it works and why it matters.
Read definition →What is Cross-Browser Testing?
Cross-browser testing verifies a web app works consistently across browsers, versions, and devices. Learn why it matters and how to do it efficiently.
Read definition →What is Quality Engineering?
Quality engineering builds quality into software across the lifecycle using automation and prevention, not late-stage testing. Learn how QE works.
Read definition →What is Quality Assurance?
Quality assurance is a process-focused discipline that prevents defects by improving how software is built. Learn how QA works and how it differs from QE.
Read definition →What is Shift-Left Testing?
Shift-left testing moves testing earlier in the software lifecycle to catch defects sooner and cut the cost of fixing them. Learn how to adopt the practice.
Read definition →What is Exploratory Testing?
Exploratory testing is a hands-on approach where testers learn, design, and run tests at once to uncover defects scripts miss. Learn how and when to use it.
Read definition →What is Continuous Testing?
Continuous testing runs automated tests across the CI/CD pipeline for instant quality feedback on every change. Learn how it enables fast, reliable releases.
Read definition →What is Test Automation Framework?
A test automation framework is a structured set of guidelines, tools, and libraries for maintainable automated tests. Learn the types and best practices.
Read definition →What is Smoke Testing?
Smoke testing is a quick check that a new software build's core functions work before deeper testing. Learn how it works, when to run it, and best practices.
Read definition →What is Sanity Testing?
Sanity testing is a focused check that a specific fix or feature works after a minor build change. Learn how it differs from smoke and regression testing.
Read definition →What is Unit Testing?
Unit testing verifies individual functions or modules of code in isolation to catch bugs early. Learn how it works, its benefits, and best practices.
Read definition →What is Integration Testing?
Integration testing verifies that combined software modules work together correctly through their interfaces. Learn how it works and its key approaches.
Read definition →What is Security Testing?
Security testing uncovers vulnerabilities, threats, and risks in software to protect data and systems from attack. Learn its types, process, and importance.
Read definition →What is Usability Testing?
Usability testing evaluates how easily real users can complete tasks in a product to improve user experience. Learn how it works and why it matters.
Read definition →What is Accessibility Testing?
Accessibility testing verifies that software is usable by people with disabilities and meets standards like WCAG. Learn how it works and why it matters.
Read definition →What is Mobile App Testing?
Mobile app testing verifies an app's functionality, usability, and performance across devices, OS versions, and networks. Learn how it works.
Read definition →What is Test Coverage?
Test coverage measures how much of your code or requirements is exercised by tests. Learn coverage types, why 100% isn't the goal, and how to use it well.
Read definition →What is Flaky Test?
A flaky test passes and fails inconsistently on the same code, eroding trust in test results. Learn common causes and how to fix flaky tests.
Read definition →What is End-to-End Testing?
End-to-end (E2E) testing validates a complete user workflow across the full stack, confirming UI, back-end, database, and integrations work together as one
Read definition →What is Load Testing?
Load testing measures how an app behaves under expected user volume, checking response times, throughput, and stability so a system stays fast and reliable
Read definition →What is Stress Testing?
Stress testing pushes an app beyond its expected limits to find the breaking point and confirm it fails gracefully and recovers, exposing capacity and
Read definition →What is Test-Driven Development (TDD)?
Test-driven development (TDD) is a practice where developers write a failing test first, then write just enough code to pass it and refactor, in short
Read definition →What is Behavior-Driven Development (BDD)?
Behavior-driven development (BDD) describes software behavior in plain, structured language so business and technical teams share understanding that doubles
Read definition →What is Visual Regression Testing?
Visual regression testing compares UI screenshots before and after changes to catch unintended visual differences in layout, color, or styling that
Read definition →What is Penetration Testing?
Penetration testing is an authorized simulated cyberattack that finds and safely exploits security weaknesses before real attackers do, validating defenses
Read definition →What is Test Data Management?
Test data management (TDM) is the practice of creating, provisioning, masking, and maintaining accurate, compliant data so tests run reliably and protect
Read definition →AI & Quality
What is Agentic AI Testing?
Agentic AI testing uses autonomous AI agents to plan, generate, run, and maintain tests under human supervision — accelerating QA while engineers stay accountable.
Read definition →What is LLM Evaluation?
LLM evaluation measures the quality, accuracy, safety, and reliability of large language model outputs using benchmarks, model-graded scoring, and red-teaming.
Read definition →What is Generative AI Testing?
Generative AI testing validates LLM-powered apps for correctness, safety, and consistency. Learn how it differs from traditional QA and why it matters.
Read definition →What is AI Testing?
AI testing validates machine learning and AI systems for accuracy, fairness, robustness, and drift. Learn what makes testing AI different from normal QA.
Read definition →What is Retrieval-Augmented Generation (RAG)?
RAG combines a retrieval system with a language model so answers are grounded in your own data. Learn how RAG works and why it reduces hallucinations.
Read definition →What is LLMOps?
LLMOps is the practice of deploying, monitoring, and managing large language model applications in production. Learn the workflow and why it matters.
Read definition →What is AI Red Teaming?
AI red teaming is adversarial testing that probes AI systems for harmful, biased, or insecure behavior. Learn how it works and why it matters for safety.
Read definition →What is Prompt Engineering?
Prompt engineering is the craft of designing inputs that steer language models to accurate, reliable outputs. Learn key techniques and why it matters.
Read definition →What is AI Governance?
AI governance is the framework of policies, controls, and oversight that keeps AI systems safe, fair, compliant, and accountable. Learn what it includes.
Read definition →What is MLOps?
MLOps is the practice of reliably deploying, monitoring, and maintaining machine learning models in production. Learn the lifecycle and core practices.
Read definition →What is Model Drift?
Model drift is the decline in a model's accuracy as real-world data shifts away from its training data. Learn its types, causes, and how to detect it.
Read definition →What is AI Hallucination?
An AI hallucination is when a model generates confident but false or fabricated information. Learn why it happens and how to reduce hallucinations.
Read definition →What is AI Agent?
An AI agent is software that uses an LLM to plan, call tools, and act autonomously toward a goal. Learn how agents work and where they help.
Read definition →What is Vector Database?
A vector database stores embeddings and finds similar items by meaning, powering RAG and semantic search. Learn how vector databases work and why they matter.
Read definition →What is Fine-Tuning?
Fine-tuning adapts a pretrained model to a specific task by training it further on a targeted dataset. Learn how fine-tuning works and when to use it.
Read definition →What is AI Observability?
AI observability is monitoring, tracing, and evaluating AI systems in production to catch drift, errors, and quality issues. Learn how it works.
Read definition →What is Prompt Injection?
Prompt injection is an attack where malicious text overrides an LLM's instructions. Learn how it works and how to defend against it.
Read definition →What is Embedding?
An embedding is a numeric vector that represents the meaning of text, images, or data so machines can compare them by similarity. Learn how embeddings work.
Read definition →What is Context Window?
A context window is the maximum amount of text an LLM can consider at once, measured in tokens. Learn how it works and why it limits AI applications.
Read definition →What is Responsible AI?
Responsible AI is the practice of building and deploying AI that is fair, transparent, accountable, and safe. Learn its core principles and how to apply them.
Read definition →Turn knowledge into shipped quality
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