Mutation Testing in Python

Overview

This project demonstrates the concept of mutation testing, a software testing technique that deliberately introduces small changes (mutants) into source code to evaluate how well a test suite can detect faults. The goal is to assess and improve test quality by measuring how effectively tests “kill” the introduced mutants.

What It Does

• Applies defined mutation operators to the Polynomial class
• Generates mutant versions of code (e.g., modified coefficients, altered logic)
• Runs the test suite against each mutant
• Reports which mutations were detected (“killed”) and which survived

Files & Structure

• original/ — Original Polynomial implementation
• mutants/ — Mutated versions of the original code
• tests/ — Unit tests to check behavior against different mutations
• README.md — This documentation

Skills Demonstrated

• Understanding of mutation testing as a technique in software quality assurance
• Python programming and test automation
• Designing mutation operators to simulate realistic faults
• Evaluating test suite effectiveness

Running It

1. Clone the repo

   git clone https://github.com/chazdj/MutationTesting.git

2. Navigate into the directory

   cd MutationTesting

3. Run the provided tests (e.g., using pytest)

   pytest

What I Learned

• Mutation testing reveals weaknesses in tests that code coverage alone might miss.
• Designing meaningful mutation operators requires understanding of both code logic and test assumptions.
• Interpreting mutant survival vs. killing helps plan stronger test cases

Recommendations for Improving the Test Suite

1. Edge Case Testing: Implement tests for extreme values of polynomial coefficients, including very large/small integers, to catch edge logic faults.

2. Parameter Sensitivity Testing: Test changes to parameters like epsilon across a wide range of values to ensure fault detection.

3. Increase Redundancy Detection: Introduce targeted test cases that check for side effects caused by redundant operations or logic missteps.

4. Expand Test Scenarios for Arithmetic Logic: Test additional combinations of operations and sequences in polynomial addition, subtraction, and multiplication.

5. Improve Input-Output Coverage: Enhance testing to explore all input combinations, particularly ones near boundary values or undefined inputs.

Author

Created by Chastidy Joanem
GitHub: @chazdj