Testing
Cause-effect graphing is the technique that decision tables grew out of. It was formalized by Myers in 1979 as a systematic way to capture the logical relationships between system inputs and outputs — and then mechanically derive a decision table from that model. Most teams skip straight to decision tables
Testing
Most testers reach for equivalence partitioning or boundary value analysis by instinct. Those are solid techniques, but they fall short when your system behavior depends on combinations of conditions. A feature that requires three inputs to all be valid before it proceeds has 2³ = 8 possible combinations, and naive testing
Testing
PractiTest's Jira integration is bidirectional: defects logged in PractiTest appear in Jira, and Jira issues can be linked as requirements in PractiTest. This covers the full setup, the field mapping configuration that trips most teams up, and how to use the traceability matrix once everything is connected. Prerequisites
Testing
PractiTest exposes a REST API that lets you create test instances, update execution status, and upload automated test results without touching the UI. This is how most teams integrate their Selenium, Cypress, or Pytest runs with PractiTest -- push results at the end of a CI job instead of manually updating
Testing
PowerMock exists because Mockito has hard limits. Mockito cannot mock constructors, cannot intercept new calls inside a method under test, and cannot directly invoke private methods for assertion. PowerMock lifts those limits by bytecode-manipulating your classes at test time. That power comes with real costs — complexity, classloader isolation, slow
Testing
Road testing autonomous driving systems is expensive, slow, and dangerous for edge cases. Waymo famously ran over 20 billion simulated miles before reaching 20 million real miles. Simulation isn't a shortcut — it's the only way to test rare scenarios like sensor occlusion in a snowstorm, or
Rust
cargo test is deceptively simple on the surface. You type it, tests run. But there's a rich set of options that most Rust developers never fully explore — options that can make your test suite faster, more precise, and easier to debug. This is a deep dive into cargo
Testing
* Postman Monitors run your collections on a schedule without any local infrastructure * Schedules can run as often as every 5 minutes from multiple global regions * Alerts fire via email, Slack, or PagerDuty when a run fails or degrades * The Monitors dashboard shows pass rate, response time, and full request history
Ruby on Rails
Capybara system tests exercise your Rails app through a real browser, catching integration bugs that unit and controller tests miss entirely. This post covers driver selection (Selenium vs Cuprite), async JavaScript patterns, screenshot-on-failure setup, and scoping techniques for complex UIs. System tests are the most expensive tests to
Testing
* Postman Flows is a visual canvas for chaining API requests without writing collection scripts * Data passes between blocks using typed connections — no pm.collectionVariables needed * Conditional (If) blocks branch the flow based on response values * Flows are shareable and runnable inside Postman — no local runtime required * Use Flows for orchestration
Testing
* Collection Runner executes all requests in a collection sequentially with pre/post scripts * Newman is the CLI equivalent — install with npm and run anywhere * Data-driven testing uses CSV or JSON files to parameterize requests * GitHub Actions and Jenkins both support Newman out of the box * newman-reporter-htmlextra produces
Testing
Deploying a new ML model version is riskier than deploying a new API version. A bad API deployment returns errors — observable, alertable, rollback-triggering. A bad model deployment returns predictions that look valid but are wrong. Accuracy can degrade 20% before a business metric moves enough to alert. By then