Sarma stangirala

Testing Stateful Code

So far, all the properties we have written have tested stateless code. Stateless code is made up of pure functions and is inherently easier to test than stateful code with side effects. The chief problem with testing stateful code is that the input to output mapping depends on the current state of the program. Previous operations can cause the same function to return different output given the same input. Therefore, in order to test stateful code, our tests must maintain some state of their own. This state is known as the model state and is updated as part of the testing process.

General Background and Overview

Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
Models and Issues in Data Stream Systems
Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
[Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep

	Table of Contents
	UNITED STATES
	SECURITIES AND EXCHANGE COMMISSION
	Washington, D.C. 20549
	____________________________________________________________________________________________
	FORM 10-K
	☒ ANNUAL REPORT PURSUANT TO SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934
	For the fiscal year ended January 28, 2024
	OR
	☐ TRANSITION REPORT PURSUANT TO SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934

	public class Main {

	public static void main(String[] args) {
	Histogram histogram = new Histogram();

	histogram.sample(0);
	histogram.sample(10.0);
	}
	}

	module Example where
	data Possible a = None \| Some a
	-- Possible is the type, None and Some are the values associated with it

	binder :: Possible a -> (a -> Possible b) -> Possible b
	binder None _ = None
	binder (Some a) f = f a

	instance (Show a) => Show (Possible a) where
	show (None) = "None"

	import uuid
	import datetime as dt
	from functools import reduce

	class Datagram(object):
	def __init__(self, val=0):
	self.val = val

	def op(self, other):
	return Datagram(self.val+other.val)

	{
	"profiles": [
	{
	"name": "Default profile",
	"selected": true,
	"simple_modifications": {
	"caps_lock": "escape",
	"f1": "mute",
	"f2": "volume_down",
	"f3": "volume_up",

	def map(intValue):
	store = {}
	store[computeBin(intValue)] += 1
	print _, store

	def reduce(_, *stores):
	_store = {}
	for store in stores:
	for k, v in store:
	_store[k] += v

	def map(intValue):
	print intValue, 1

	def reduce(*args):
	store = {}
	for intValue, count in args:
	store[computeBin(intValue)] += count

	from collections import defaultdict

	class Monoid():
	def __init__(self, zero, foldOp, liftFunctor=None):
	self.store = defaultdict(zero)
	self.liftFunctor = liftFunctor
	self.foldOp = foldOp
	self.zero = zero

	def fold(self, key, vals):