Fault-based testing, particularly mutation testing, is a method used to evaluate the efficacy of test cases by inserting artificial bugs, or mutants, into the code to assess whether the test set can distinguish between the original and the modified versions. This approach is underpinned by the competent programmer hypothesis and the coupling effect, suggesting that by identifying simple faults, a large percentage of complex faults can also be detected. Mutation testing involves creating mutants through slight code modifications and aims to "kill" them by ensuring test cases expose any differences in behavior between the original code and its mutants. The effectiveness of a test set is measured by its ability to eliminate these mutants, and historical data can guide the need for additional test cases if mutant-killing efficiency decreases. The pesticide paradox, which suggests tests become less effective over time if repeated without changes, is challenged here, as mutation testing maintains its utility unless software requirements evolve. Despite potential challenges in finding equivalent mutants, mutation testing provides a robust framework for enhancing software quality by ensuring a comprehensive and dynamic approach to test design.