Algorithmic Analysis of Data Structures
From charlesreid1
Algorithmic analysis
While each data container is implemented differently, due to different tradeoffs between storage space and algorithmic complexity, it is useful to define a set of core tests for algorithmic analysis of data structures.
We begin with lists, which are arguably the simplest.
Big O complexity analysis
To perform a big O complexity analysis, we utilize random but seeded inputs that statistically sample the input space in a representative way. This ensures we aren't getting stuck measuring the runtime of a worst-case or best-case, and thus getting a skewed view of the algorithm.
Corner cases and other kinds of timing tests, like adding and removing at a high, balanced rate and having a near-empty queue with high throughput.
Memory usage
No way to measure memory usage of objects in-memory, left on our own performing memory profiling.