Text Mutation System

Alexander Goddard, Abishek Dhakal, Joesph Oforkansi, Ritesh Ojha

September 1, 2025

Source code & Explanation

• What does this script do?
  • Reads a text file (input.txt)
  • Mutates characters randomly based on a mutation rate
  • Saves the mutated text to (mutated.txt)
  • Useful for testing algorithms or simulating text variations

import random
import string

# Path of text file
path = "input.txt"

# Read the original text
with open(path, "r") as file:
    text = file.read()
print("Original Text:", text)

# Function to randomly mutate text
def mutate_text(text, mutation_rate=0.1):
    mutated = ""
    for char in text:
        if random.random() < mutation_rate:
            mutated += random.choice(string.ascii_letters)
        else:
            mutated += char
    return mutated

# Apply mutation with 20% mutation rate
mutated_text = mutate_text(text, mutation_rate=0.2)
print("Mutated Text:", mutated_text)

# Save the mutated text to a file
with open("mutated.txt", "w") as file:
    file.write(mutated_text)   

Create an Example Text File

Before we can run our mutation engine we need a text file

Test the mutation engine!

Now that we have a text file we can test our mutation engine

Tractable & Why Our Text Mutation Engine is Tractable

What is Tractable?

• A problem is tractable if a computer can solve it in a practical amount of time, even with a huge amount of data.
• The time it takes to solve the problem grows predictably, like a straight line on a graph, not an exploding curve. We call this a linear O(n) or polynomial O(n²) rate.
• Example: Imagine sorting a list of names. It takes a reasonable amount of time whether you have 10 names or 10 million.
• Non-tractable example: Trying to find the best possible move in every single chess game ever played. The number of possibilities is so vast it would take a supercomputer trillions of years.

Why Our Text Mutation Engine is Tractable

• The engine works by checking each character one at a time. Each character is processed in a very quick, constant amount of time.
• The total processing time grows linearly with the text size (known as O(n) complexity), meaning it remains fast even with very large files.

Results and Q&A

• Fast & Scalable: Efficiently processes large texts and dynamically adjusts the mutation rate to control the extent of content transformation.🚀

• Effective Mutation: The system applies random mutations to the text, altering roughly 20% of characters on average per run. The actual percentage may vary due to randomness, producing different variations each time.

• Real-World Application: While the engine is highly effective, a notable limitation is the production of non-words. This highlights an opportunity to integrate a dictionary or grammar check for more advanced applications.

• Example Mutation:

  • Original: “This is Group three”
  • Mutated: “This is Gorud thoee”

• Thank You. We’re now open to any questions you may have.