Implementing A/B Testing Logic in Go

This challenge focuses on building the core logic for an A/B testing system in Go. A/B testing is a crucial technique for product development and marketing, allowing teams to experiment with different versions of a feature or design to determine which performs better. You will implement a system that can assign users to different experiment variants.

Problem Description

You need to create a Go program that simulates the assignment of users to different variants (A or B) in an A/B test. The assignment should be deterministic for a given user and a given experiment, meaning the same user should always be assigned to the same variant for the same experiment. The assignment should also be distributed as evenly as possible between the variants.

Key Requirements:

1. User-to-Variant Assignment: Given a userID (a string) and an experimentName (a string), the system should return either "A" or "B" representing the assigned variant.
2. Deterministic Assignment: For a fixed userID and experimentName, the function must always return the same variant.
3. Even Distribution: Over a large number of unique users, the distribution of assignments between variant "A" and variant "B" should be approximately 50/50.
4. Experiment Independence: Assignments for different experiments should be independent of each other. A user assigned to variant "A" for experiment1 could be assigned to variant "B" for experiment2.

Expected Behavior:

The core of your solution will be a function that takes userID and experimentName and returns the assigned variant. You should also demonstrate how to use this function with sample data.

Edge Cases:

• Empty userID or experimentName: While ideally these would be validated beforehand, consider how your hashing mechanism might handle them. For this challenge, assume valid, non-empty strings.
• Collisions in hashing: While unlikely with good hashing functions, consider the implications if two different user/experiment combinations produced the same hash. Your solution should gracefully handle this.

Examples

Example 1:

Input:
userID = "user123"
experimentName = "new_homepage_layout"

Output:
"A"

Explanation: For the input userID="user123" and experimentName="new_homepage_layout", the system deterministically assigns the user to variant "A".

Example 2:

Input:
userID = "user456"
experimentName = "new_homepage_layout"

Output:
"B"

Explanation: For the input userID="user456" and experimentName="new_homepage_layout", the system deterministically assigns the user to variant "B". If userID was "user123" and experimentName was "checkout_button_color", it might still result in "A" or "B" independently.

Example 3:

Input:
userID = "user123"
experimentName = "checkout_button_color"

Output:
"B"

Explanation: This demonstrates experiment independence. The same userID ("user123") is now being tested on a different experiment ("checkout_button_color") and is assigned to variant "B".

Constraints

• The userID and experimentName will always be non-empty strings.
• The solution should be implemented in Go.
• The assignment algorithm should aim for an approximate 50/50 split for a large, random set of users. A simple modulo operation on a hash is acceptable.
• Avoid external libraries for the core assignment logic if possible, but using standard Go libraries like hash/fnv or crypto/md5 is permitted.

Notes

• Hashing: To achieve deterministic assignment, you'll need to combine the userID and experimentName and then hash this combined string. A common approach is to concatenate them (e.g., experimentName + ":" + userID) and then hash the result.
• Distribution: After hashing, you'll need to map the hash output to either variant "A" or "B". A common method is to take a portion of the hash (e.g., the first few bytes or a specific integer representation) and apply a modulo operation to determine the variant. For a 50/50 split, you'd typically use modulo 2.
• Key Generation: Think about how to create a unique key for each user-experiment pair that can be reliably hashed.
• Success Criteria: A successful implementation will correctly and deterministically assign users to variants based on their userID and experimentName, and exhibit a near 50/50 split when tested with a large number of unique users.