Code Complexity Talk

Transcript

1. Code Complexity Eduardo Maciel (@edumaciel10)
2. Resume - Big O Notation - How and why codebase grows - How the complexity works - How to avoid complexity
3. Code Complexity - Time of running/compilation - Memory used - Difficult to understand - Money wasted
4. Code Complexity Cost - More time fixing bugs - More time making refactor - The new code can grow wrong
5. Code Complexity Cost - We want write more code instead read more code - Robert C. Martin pointed out that “the ratio of time spent reading versus writing is well over 10 to 1.”
6. Codebase growing - What will happening if each dev of our team add on every day a new code? - We don’t want to make irreversible decisions - We don’t want to make to much coupled code
7. Methods to solve - Processes - Patterns - Documentation - Continuous Integration
8. Most similar process
9. O notation - It is the relative representation of the complexity of an algorithm. - It describes how an algorithm performs and scales. - It describes the upper bound of the growth rate of a function and could be thought of the worst case scenario.
10. O notation
11. O notation - We are company that think in grow on 10x, 100x or 1kk, for this is needed algorithms and ideas that scale and grow with health
12. Entropy Mindustry
13. Entropy