Aaron Kaw focused on enhancing the reliability of symbolic differentiation in the JuliaSymbolics/Symbolics.jl repository by developing comprehensive consistency tests. He implemented new unit tests to verify that derivative calculations yield consistent results when functions are applied directly or through the BasicSymbolic representation. This work, rooted in software development and symbolic computation, aimed to strengthen the engine’s correctness and maintainability by expanding test coverage and integrating early regression detection. Although no bugs were fixed during this period, Aaron’s contributions provided a deeper foundation for ongoing maintenance, ensuring that future changes to the symbolic differentiation engine remain robust and reliable.