How would that affect the correctness and efficiency of the solution?

Changing the algorithm's core logic can have a direct impact on both correctness and efficiency. For correctness, any modification that alters the invariants or assumptions of the algorithm may lead to incorrect results or edge‑case failures. For example, if you replace a stable sorting algorithm with an unstable one in a context where relative order matters, the output will be wrong. For efficiency, even a small change can affect time or space complexity. Switching from a linear‑time O(n) solution to a quadratic O(n²) one will drastically increase runtime on large inputs. Conversely, optimizing a loop to reduce constant factors can improve performance without affecting correctness. Therefore, before making changes, you should analyze the algorithm's invariants, test against edge cases, and evaluate the complexity trade‑offs. In practice, it's common to maintain a suite of unit tests that cover typical scenarios, boundary conditions, and stress cases. Additionally, profiling tools can help identify performance bottlenecks before they become critical. By combining rigorous testing with careful complexity analysis, you can confidently modify code while preserving correctness and efficiency.