In **Competitive Programming**, it’s quite often to write code that works on the given sample test cases but on submitting its end up with a **WA**(Wrong answer) verdict. This is where things get frustrating and not know on which **test case** the solution **fails**. In such situations there are a few things that a **programmer** can do:

- Check manually for corner cases
**Stress Testing**

__Checking for Corner Cases__:

__Checking for Corner Cases__:

In some problem-statement, it has **corner test cases** where the programmer needs to **add/edit** some lines of code to make sure the program works fine for those cases as well. For example, in a problem where it needs to find the **Minimum Positive Integer** in an array and if a solution something like this:

int mn = arr[0]

for i : 1 -> arr_size:

if (arr[i] < mn)

mn = arr[i]

print mn

__Explanation__:

The above logic would not give an **AC**(Accepted) verdict and it is easy to figure out the problem here. One of the **test cases** where this code will fail is when the array is **arr[] = {2, -1, 3, 4, 5}**. The above algorithm Output is **-1** But the correct Output is **2**.

For finding the minimum **positive **integer, it needs to edit the code to make sure negative integers **must not** be included.

__Stress Testing__:

__Stress Testing__:

**Stress Testing** is a code testing technique that determines the **robustness** of code by testing beyond the **limits** of normal operation. Suppose a problem which has a **naive solution**(slow) and an **optimal solution**(fast) gets a **TLE** (Time Limit Exceeded) verdict on submitting the brute force solution, so come up with the optimal solution but on submitting it we get **WA** on some or all test cases.

Now the first thing to do here would be to think of some **test cases** where the solution may not work and check the solution on them. If unable to think of the test cases then there is one more way and that is stress testing**. **

In **stress testing**, the idea is to generate random test cases and check the output of the brute force solution and the optimal solution. Though the brute force solution is **slow** still it’s **correct** while the optimal solution is **faster** but it’s wrong in some test cases. This allows us to check if the optimal solution’s output of some test case is correct or not without **manually **checking, it can simply check if the output of **Naive Solution** agrees with the **Optimal Solution’s** output. The checking is done automatically and also the correctness of code on thousands depends on the **complexity **of the **Naive Solution** of test cases in seconds, so the **probability** of finding the test case where our code fails becomes way **high**.

So, perform the below steps to check the solution on random input by running an infinite loop:

- Generate
**random**input (click here to know how) - Store output of brute force and
**optimal**solution - If the two outputs are (equal) than print
**correct** - Else print the input and
**break**the loop

If the loop runs for some time without breaking i.e., all the outputs are correct then either check for larger input or try submitting your solution.

**Example:**

** Problem Statement:** Given an array

**arr[]**of

**N**positive Integers, the task is to find the maximum pairwise product of elements in an array.

Input:arr[] = [2, 3, 4, 9, 4, 7]Output:63Explanation:

The maximum pairwise product of the array is 9 * 7 = 63.

Input:arr[] = [-20, -50, 1, 2]Output:1000Explanation:

The maximum pairwise product of the array is (-20) * (-50) = 1000.

Below is the implementation of **stress testing** for the above problem:

## C++

`// C++ program to illustrate the stress ` `// testing for the above problem ` `#include <bits/stdc++.h> ` `using` `namespace` `std; ` ` ` `// Function that implements the Naive ` `// Solution for the given problem ` `int` `maxProductNaive(` `int` `a[], ` `int` `n) ` `{ ` ` ` `int` `ans; ` ` ` `for` `(` `int` `i = 0; i < n; i++) { ` ` ` `for` `(` `int` `j = i + 1; j < n; j++) { ` ` ` `if` `(i == 0 && j == 1) ` ` ` `ans = a[i] * a[j]; ` ` ` `else` `if` `(a[i] * a[j] > ans) ` ` ` `ans = a[i] * a[j]; ` ` ` `} ` ` ` `} ` ` ` `return` `ans; ` `} ` ` ` `// Function that implements the Optimal ` `// Solution for the given problem ` `int` `maxProductOptimal(` `int` `a[], ` `int` `n) ` `{ ` ` ` `// Sort the given array ` ` ` `sort(a, a + n); ` ` ` ` ` `// Find the maximum product ` ` ` `int` `ans = a[n - 1] * a[n - 2]; ` ` ` ` ` `// Return the product ` ` ` `return` `ans; ` `} ` ` ` `// Function that performs the ` `// Stress Testing ` `void` `test() ` `{ ` ` ` `// Seeding random number generator ` ` ` `// to get uniques values ` ` ` `srand` `(` `time` `(0)); ` ` ` ` ` `while` `(1) { ` ` ` ` ` `// Generate values for n ` ` ` `// from 2 to 10 ` ` ` `int` `n = ` `rand` `() % 10 + 2; ` ` ` `int` `a[n]; ` ` ` ` ` `// Iterate over array a[] ` ` ` `for` `(` `int` `i = 0; i < n; i++) { ` ` ` ` ` `// Subtracting -5 will ` ` ` `// generate -ve integers ` ` ` `a[i] = ` `rand` `() % 10 - 5; ` ` ` `} ` ` ` ` ` `// Solution using Naive Approach ` ` ` `int` `ans_brute ` ` ` `= maxProductNaive(a, n); ` ` ` ` ` `// Solution using Optimal Approach ` ` ` `int` `ans_optimal ` ` ` `= maxProductOptimal(a, n); ` ` ` ` ` `// If ans is correct ` ` ` `if` `(ans_brute == ans_optimal) ` ` ` `cout << ` `"Correct\n"` `; ` ` ` ` ` `// Else print the WA Test Case ` ` ` `else` `{ ` ` ` `cout << ` `"Wrong Answer\n"` `; ` ` ` ` ` `cout << n << endl; ` ` ` ` ` `// Print the array a[] ` ` ` `for` `(` `int` `i = 0; i < n; i++) ` ` ` `cout << a[i] << ` `" "` `; ` ` ` ` ` `cout << ` `"\nCorrect Output: "` ` ` `<< ans_brute << endl; ` ` ` ` ` `cout << ` `"Wrong Output: "` ` ` `<< ans_optimal << endl; ` ` ` `break` `; ` ` ` `} ` ` ` `} ` `} ` ` ` `// Driver Code ` `int` `main() ` `{ ` ` ` `// Function Call for Stress Testing ` ` ` `test(); ` ` ` `return` `0; ` `} ` |

*chevron_right*

*filter_none*

**Output:**

Wrong Answer 4 -4 -3 -3 1 Correct Output: 12 Wrong Output: -3

__Explanation__:

The Optimal Solution works fine for positive integers but fails on **negative integers **like shown in the output of the code. Through stress test it was figured out the problem with the code. To generate and test our code for larger input, generate larger integers for **n **and **a[I].**

__Summary__:**Stress testing **will surely help in **debugging **the code but first should try to think of test case where the code **may not **work because its less **complex** to check for some simple test cases and if that **doesn’t **help then proceed with the **Stress Testing**.