#Testing
37 articles tagged with #Testing
Testing Python Code with pytest: A Beginner's Guide
Untested code is legacy code from the moment it's written. This guide explains how to write effective Python tests with pytest — from your first test function through fixtures, parametrize, mocking, and measuring coverage.
Statistics for Data Science: The Essentials
The core statistics every data scientist needs: distributions, sampling, probability, hypothesis testing, and correlation, explained in plain language.
CI/CD Explained: Ship Code Faster and Safer
CI/CD automates building, testing, and releasing code so teams ship small changes often with less risk. Learn the pipeline stages and best practices.
How to Evaluate an AI Startup Idea in 2026
Evaluate an AI startup idea by testing whether it solves a real painful problem, has a durable data or workflow moat, and survives when foundation models improve.
How to Debug Code Like a Professional
Debugging like a pro means reproducing the bug, forming a hypothesis, and testing it methodically. Learn the systematic process and the tools that speed it up.
What Is Test-Driven Development (TDD)?
Test-driven development is writing a failing test before the code that makes it pass. Learn the red-green-refactor cycle, its benefits, and how to start.
What Is A/B Testing? A Data-Driven Guide
A/B testing compares two versions of something to see which performs better using real data. Learn how to design, run, and interpret experiments correctly.
How to Write Your First Unit Test
A unit test checks one small piece of code in isolation. Learn to write your first test with the Arrange-Act-Assert pattern using a modern JavaScript test runner.
What Is Hypothesis Testing in Statistics
Hypothesis testing is a method for deciding whether data supports a claim about a population. Learn null vs alternative hypotheses, p-values, and errors.
A/B Testing Explained for Aspiring Analysts
A/B testing explained for aspiring analysts: form a hypothesis, size your sample, read p-values correctly, dodge common pitfalls, and interpret results with confidence.
Software Development Life Cycle: The 7 Phases Explained
The software development life cycle (SDLC) is the structured process teams follow to plan, build, test, and maintain software, made up of phases like requirements, design, coding, testing, deployment, and maintenance.
What Does a Phlebotomist Do, and How Do You Become One?
A phlebotomist is a trained healthcare professional who draws blood from patients for testing, transfusions, donations, or research. This guide explains the daily role, required training, and the skills that make a good phlebotomist.
What Does a QA Tester Do? A Practical Overview
A QA tester finds defects in software before customers do, by designing test cases, executing them, and reporting bugs clearly enough for developers to fix. This guide explains the daily role, key skills, and how manual and automated testing differ.
A/B Testing Explained: How to Run a Valid Experiment
A/B testing compares two versions of something to see which performs better with real users. This guide covers how a valid test is structured, common pitfalls, and how to read results without fooling yourself.
Quality Assurance Analyst: Role, Skills, and Career Path
A quality assurance analyst tests software to catch defects before release, combining structured testing with clear bug reporting. This guide covers the role's daily work, required skills, and how to build a QA career.
Programmer vs Developer: What's the Real Difference?
A programmer typically focuses on writing code to solve a specific problem, while a developer usually owns a broader process such as design, testing, and delivery around that code. The terms overlap far more than they conflict in practice.
What Is Ethical Hacking? A Beginner's Guide to the Field
Ethical hacking is the authorized practice of testing systems for security weaknesses so organizations can fix them before real attackers find them. This guide covers what the role involves, the skills it requires, and how it differs from malicious hacking.
What Is Unit Testing? A Practical Guide with Examples
Unit testing means verifying that the smallest testable pieces of your code, usually individual functions, behave correctly in isolation. This guide explains how unit tests work, how to write good ones, and why they catch bugs before users ever see them.
How to Start a Startup: A Practical Step-by-Step Guide
Starting a startup means validating a real problem, building a minimal product, and testing whether people will pay for the solution before scaling. This guide walks through the practical steps in order, from idea to first customers.
What Is User Acceptance Testing? A Clear Definition
User acceptance testing is the final testing phase where real users confirm software meets their actual needs before release. This guide explains what UAT involves, how it differs from QA testing, and how to run one effectively.
What Does a QA Manual Tester Do?
A QA manual tester verifies software behaves correctly by executing test cases by hand, without automation scripts. This guide explains the role's daily responsibilities, core skills, and how it differs from automated testing.
What Is Smoke Testing? A Quick QA Explainer
Smoke testing is a quick round of checks run on a new build to confirm the most critical features work before deeper testing begins. This guide explains what it covers, why teams rely on it, and how it fits into a wider testing strategy.
Business Statistics 101: How Data Drives Decisions
Business statistics applies statistical methods to real commercial questions, from forecasting demand to testing whether a marketing change actually worked. This guide covers the core concepts every analyst and manager should understand.
Analytics Engineering With dbt: Modelling the Warehouse
dbt makes SQL transformation behave like software: models in version control, tests that fail a build, dependencies resolved from a graph, and documentation generated from the code that produces the tables. This article covers the modelling layers, the testing strategy and the failure modes that appear as a project grows.
Statistical Inference for Practitioners: Sample to Decision
Inference is one workflow, not a box of formulas: you sample, you estimate with uncertainty, you decide, and you state what would change your mind. This guide connects sampling variation, estimation, testing and decision-making so the tools stop feeling arbitrary and start answering the question you actually asked.
Python Testing Explained: Fixtures, Mocks, and Coverage
A good Python test suite is layered: fast isolated tests for logic, slower integration tests against real dependencies, and a small number of end-to-end checks. This guide sets out that spectrum, the pytest features that make each layer manageable, and how to tell whether your suite is actually trustworthy.
Forecasting Time Series: Choosing a Model and Proving It Works
Start with a naive baseline, add candidate models only when they beat it, and evaluate everything with rolling-origin backtesting that never lets future data inform a past prediction. This guide walks the full protocol: diagnosing the series, choosing model families, selecting metrics that survive your data, and detecting leakage before deployment.
How to Build a Golden Dataset for LLM Testing
A golden dataset is a fixed, versioned set of inputs with reference answers and grading criteria, drawn from real usage and deliberately seeded with adversarial cases. This covers how to select cases, write references that survive rewording, keep the set honest as it ages, and avoid the failure of testing only what already works.
How to Build an MCP Server for Your Internal Tools
Building an MCP server means wrapping internal APIs as named tools with strict input schemas, explicit auth boundaries and error messages a model can act on. This covers choosing what to expose, writing schemas that prevent bad calls, handling credentials, shaping responses for context budgets and testing before an agent touches production.
How to Run Prompt Regression Tests in CI
Prompt regression testing means running a fixed case set on every change and failing the build when aggregate scores drop below a stored baseline. This article covers wiring evals into a pipeline, choosing thresholds for non-deterministic output, and keeping the suite fast enough to survive.
Why the Same Prompt Returns a Different Answer Each Time
Identical prompts diverge for three separate reasons: sampling picks different tokens, server-side batching changes floating-point reduction order, and infrastructure shifts underneath you. Only the first is fully in your control, so the practical goal is not determinism but bounding variation and testing for behaviour rather than exact strings.
How to backtest a forecast with rolling-origin evaluation
Rolling-origin backtesting picks a cut-off, fits on history only, forecasts the full horizon, then rolls the cut-off forward and repeats. Aggregate the errors by horizon step rather than overall, because a model can be excellent one step ahead and useless at the horizon the business plans on. This article walks the mechanics and the choices inside them.
Pytest fixtures explained: scope, teardown and conftest
Fixture scope is a correctness decision before it is a speed one. This walks through what each of the four scopes shares, why the yield form is the right way to tear down, how conftest.py discovery decides which tests can see a fixture, and how shared mutable state turns a green suite order-dependent.
How to refactor legacy code that has no tests
On untested legacy code you do not write unit tests first. You pin the current behaviour with characterisation tests at the widest boundary you can already call, then break dependencies inward. This article gives the order of operations and the seams that make it possible.
When to use a Python set instead of a list
The decision is membership testing: a set answers whether an item is present in near-constant time while a list scans every element, and that difference only matters when the check happens inside a loop. Here is the break-even, and the ordering and hashability you trade away.
How to write parametrised tests in pytest
Parametrisation collapses near-identical tests into one data-driven case list — but only when the cases differ purely in data. This covers the basic form, readable IDs so a failure names the case, stacking for combinations, per-case marks, and the point at which a separate named test is clearer.
Testing language model outputs for bias in a real application
Generic bias benchmarks say little about your feature. Build counterfactual test sets from your own inputs and measure differential behaviour you can act on.