
Developed a deterministic random number generator module for the CSE498/CSE498-Spring2025 repository, focusing on reproducible simulations across multiple numeric types. Designed and implemented a C++ Random class with a seedable API, supporting uniform generation of doubles, integers, and probabilities, while consolidating code into a modern header-only structure. Enhanced reliability by establishing comprehensive unit testing infrastructure and refining build workflows using Makefile and clang-format. Addressed error handling through assertions and improved code maintainability with updated header guards. The work emphasized robust software design, test-driven development, and build system management, resulting in a reusable randomness component tailored for simulation and numerical experiments.
February 2025 monthly summary for CSE498-CSE498-Spring2025. Focused on delivering a robust Random module API and establishing testing infrastructure to ensure reliability of randomness utilities used in simulations and numerical experiments. Key work spanned API surface, header consolidation, and test/build workflow improvements.
February 2025 monthly summary for CSE498-CSE498-Spring2025. Focused on delivering a robust Random module API and establishing testing infrastructure to ensure reliability of randomness utilities used in simulations and numerical experiments. Key work spanned API surface, header consolidation, and test/build workflow improvements.
Month: 2025-01 — Delivered initial specifications for a Deterministic Random Number Generator (RNG) class to support reproducible simulations across multiple numeric types. This foundational work defines seeding for reproducibility and uniform generation for doubles, integers, and probability values, with error handling and cross-type usage considerations. No major bug fixes recorded this month.
Month: 2025-01 — Delivered initial specifications for a Deterministic Random Number Generator (RNG) class to support reproducible simulations across multiple numeric types. This foundational work defines seeding for reproducibility and uniform generation for doubles, integers, and probability values, with error handling and cross-type usage considerations. No major bug fixes recorded this month.

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