The recent trouble with the Airbus A320 — caused by a critical software bug — shows why rigorous testing is non-negotiable in complex systems. The “A320 bug” — linked to the aircraft’s ELAC (elevator/aileron control) software — was triggered by intense solar radiation that can corrupt flight-control data. As a result, dozens of international carriers were forced to ground or recall thousands of jets: it’s arguably the largest aircraft-fleet recall in aviation history. Had the faulty version of the software been subjected to more exhaustive testing — including scenarios such as solar-radiation-induced data corruption — the vulnerability might have been caught before commercial deployment. A robust test suite would have included “edge cases” (rare but plausible events) to stress-test the code under extreme conditions. The root cause: under periods of “intense solar radiation” (solar flares / charged particles), the electronic data processed by ELAC software can get corrupted — a bit-f...
Google may be the most popular search engine, but it isn’t always the best for academic and scientific research. Many valuable scholarly resources are buried under commercial results or hidden behind paywalls. Fortunately, there are specialized search engines designed to help researchers, students, and professionals find high-quality, peer-reviewed content. Here’s a selection of the best alternatives to Google, focusing on science, technology, medicine, and economics. 1. RefSeek ( www.refseek.com ) Best for: General academic research. RefSeek indexes over a billion documents, including research papers, encyclopedias, and books. Unlike Google, it prioritizes educational content and filters out commercial websites. 2. WorldCat ( www.worldcat.org ) Best for: Finding books and research materials in libraries worldwide. WorldCat allows users to search for books, articles, and historical archives in over 20,000 libraries. It’s ideal for tracking down rare or specialized academic ma...
The T-shirt sizing (XS, S, M, L, XL, XXL) is a popular estimation technique that often gets converted to story points. Here's how they typically relate: Standard T-Shirt to Story Point Conversion Common Mapping: XS (Extra Small): 1 story point S (Small): 2-3 story points M (Medium): 5 story points L (Large): 8 story points XL (Extra Large): 13 story points XXL (Extra Extra Large): 21+ story points This often follows the Fibonacci sequence (1, 2, 3, 5, 8, 13, 21) which reflects increasing uncertainty in larger estimates. [Each number is the addition of preceding 2 numbers] T-Shirt Sizing to Hours (Rough Guidelines) Typical Team Patterns: XS Stories: 1-4 hours Simple bug fixes, minor text changes "I can do this in half a day" S Stories: 4-8 hours Small feature additions, straightforward tasks "This will take me a day or less" M Stories: 1-3 days (8-24 hours) Standard feature development "This is a typical user story" L Stories: ...
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