Test Reporting
As test automation grows from dozens to thousands of test cases, understanding why tests are failing becomes harder than running them. ReportPortal addresses this directly: it's an open-source test reporting and analytics platform built for teams that run large-scale automation. Unlike simple JUnit report viewers, ReportPortal applies machine
Haskell
Example-based testing asks "does this function return the right value for this specific input?" Property-based testing asks something harder: "does this function behave correctly for every possible input?" QuickCheck, the original property-based testing library that spawned a dozen ports across languages, answers that question by generating
Node.js
Building a CLI tool with oclif is pleasantly structured. Each subcommand is a class with a run() method. Flags and args are declared statically. The framework handles parsing, help generation, and error formatting. The problem is that most teams treat this structure as incidental and test nothing until something breaks
Mutation Testing
Code coverage tells you which lines executed during tests. Mutation score (MSI) tells you whether your tests would catch a change in behavior. These measure different things. Coverage is cheap and fast. Mutation testing is expensive and slow. The right answer is to use both — coverage as a floor that
Python
mutmut is the most widely adopted Python mutation testing tool. It works by making small, targeted modifications to your source code — flipping + to -, changing True to False, removing return values — and running your test suite against each modification. If your tests don't fail when the code changes,
Test Reporting
Most CI pipelines collect test results and then throw them away. The JUnit XML file gets archived, the pipeline moves on, and nobody looks at the trend until something catastrophically breaks. A Grafana dashboard changes that. By routing test result data into a time-series store, you get visibility into pass
iOS Testing
SwiftUI made building iOS UIs faster and more composable than ever. It also introduced a testing challenge that UIKit developers did not face at the same scale: because SwiftUI views are value types driven entirely by state, there is no single correct way to test them. You cannot instantiate a
PHP
Code coverage is one of those metrics that developers either obsess over or dismiss entirely. The truth sits between those extremes: coverage data is genuinely useful for finding untested code paths, but a 90% coverage number tells you nothing about whether those tests are actually meaningful. Understanding what coverage measures
Shell Testing
Most shell testing frameworks assume you are running bash. Real infrastructure does not. Log processors run under POSIX sh on Alpine Linux. Deployment scripts run under ksh on AIX. Init scripts run under dash on Debian. If your tests only work with bash, you will not catch the bugs that
Rust
Rust has a feature that no other mainstream language matches: every code example in your documentation is compiled and run as a test by default. There is no special setup, no framework to install, no annotation to add. Write a /// ``` rust block in your doc comment, and cargo test runs
Python
Documentation lies. Not because developers intend to mislead, but because code evolves and docstrings do not. A function gets refactored, its return format changes, and somewhere in the codebase an outdated >>> example claims it returns a list when it now returns a generator. The user copies that
OCaml
OCaml has a strong type system that catches many bugs at compile time, but types can't verify that your sorting algorithm actually sorts or that your parser handles malformed input gracefully. For that you need tests. Alcotest is OCaml's most popular lightweight test framework — it'