Tree Rewriting And Shunting Yard Parsers

This entry is part 2 of 2 in the series Handling Associativity And Precedence in Handwritten Parser

Introduction Last time we discussed our mission, built a lexer and tree printer to be used throughout our experiments, and introduced the Recursive decent parser. Parsing mathematical expressions involves interpreting their structure, which can be complex due to the presence of operators with different precedence levels and associativity rules. In this article series, we delve into

Handling Associativity and Precedence in Handwritten Parsers

This entry is part 1 of 2 in the series Handling Associativity And Precedence in Handwritten Parser

In the context of Abstract Syntax Trees (ASTs) and parse trees, the terms “higher” and “lower” precedence, as well as tree “depth,” take on a different meaning due to the way parsers traverse the tree structure. In this context, “higher” precedence refers to nodes that are deeper in the tree, further away from the root. When parsers descend into the tree to evaluate expressions or execute algorithms, they typically start at the root and move downwards towards the leaves. Therefore, nodes that are deeper in the tree, or have a higher depth, are processed first, followed by nodes closer to the root. Conversely, nodes closer to the root have lower depth and are processed later in the parsing or evaluation process. This understanding is crucial for parsers and algorithms that rely on tree traversal to correctly interpret and evaluate expressions or perform other operations on tree structures. I have heard these terms used in reverse and for trees in general, this may be correct. I mention this because it can cause confusion, it is worth clarifying these terms when conversing with others.

Understanding IEEE 754 Double Precision Floating Point Values By Implementing it in Python

Discover the world of IEEE 754 Double Precision Floating Point in Python with our in-depth exploration. Dive into the Python struct module, understand the nuances of floating-point representation, and grasp the essentials of numerical computing. Gain hands-on experience with the provided Python code for IEEE 754 implementation, unraveling the complexities of sign bits, exponents, and fractions.

An Adventure in Coding with QB64: A Beginner’s Guide for Young Minds

This entry is part 1 of 2 in the series Coding For Kids

Embark on a magical coding journey with QB64, a modern version of BASIC designed for young minds. Learn the basics of programming, create personalized spells, explore data types, and engage in enchanting exercises. Let your creativity soar as you master the art of coding!

🚀 Exciting News: Introducing “Coding For Kids” – A New Series on CodeRancher! 🚀

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🚀 Introducing “Coding For Kids” – A New Series on CodeRancher! 🚀 Dive into the world of coding with our exciting new series designed for young tech enthusiasts aged 7 to 12. Experience hands-on, story-driven coding adventures that make learning computer science fun and engaging. Stay tuned for interactive puzzles, real-world applications, and the joy of unleashing creativity through coding! Let the coding adventures begin! 🚀

Is Software Development An Engineering Discipline, Science, or Craft? We Ask ChatGPT!

Discover the intricate balance between theory and practice in software development. This analysis explores the intersection of software development with science, engineering, and trade craft disciplines. Gain insights into the empirical nature, problem-solving approaches, and tools used in this hybrid discipline.

Modern C Programming: Embracing New Features, Standards, and Historical Changes

Delve into the evolution of C programming, from historical changes to embracing new features like C11. Explore its enduring usefulness in modern software development and understand its impact on programming languages and the fast-paced world of technology.

Rebuttal to Eric Normand’s Article: “How is Haskell Faster than C?”

Delve into the nuanced debate between Haskell and C programming languages. This article dissects the claims about Haskell’s speed superiority over C, emphasizing the importance of well-crafted code and developer expertise in determining programming efficiency. Discover the complex interplay of factors that influence the performance of these languages in real-world applications.