AI, Engineering Assistance - Code Modification

Summary

Artificial intelligence techniques, such as machine learning and natural language processing, can be incredibly useful for code modification with bug detection and fixing, refactoring, and optimization.

CODE REFACTORING

Employ AI to suggest code refactoring options, improving code readability, and optimizing performance.

Tools: IDE plugins or extensions equipped with AI-based code analysis and refactoring capabilities.

Example Prompt: Make the following code more readable:

def find_intersection(A, B, C, D):
	x1, y1 = A
	x2, y2 = B  
	x3, y3 = C  
	x4, y4 = D  
	denominator = (x1 - x2) * (y3 - y4) - (y1 - y2) * (x3 - x4)
	if denominator == 0:
		return None # Parallel lines, no intersection  
	x = ((x1 * y2 - y1 * x2) * (x3 - x4) - (x1 - x2) * (x3 * y4 - y3 * x4)) / denominator
	y = ((x1 * y2 - y1 * x2) * (y3 - y4) - (y1 - y2) * (x3 * y4 - y3 * x4)) / denominator
	return x, y

AI

Response:

def find_intersection(line1_point1, line1_point2, line2_point1, line2_point2): # Unpacking coordinates for better readability  
 x1, y1 = line1_point1  
 x2, y2 = line1_point2
 x3, y3 = line2_point1
 x4, y4 = line2_point2
 
 # Calculate the denominator for finding intersection
 denominator = (x1 - x2) * (y3 - y4) - (y1 - y2) * (x3 - x4)
 
 # Check if lines are parallel
 if denominator == 0:
 	return None # Lines are parallel, no intersection
 
 # Calculate intersection point
 x_intersection = ((x1 * y2 - y1 * x2) * (x3 - x4) - (x1 - x2) * (x3 * y4 - y3 * x4)) / denominator
 y_intersection = ((x1 * y2 - y1 * x2) * (y3 - y4) - (y1 - y2) * (x3 * y4 - y3 * x4)) / denominator
 
 return x_intersection, y_intersection

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BUG FIXING

Use AI algorithms to identify potential bugs or anomalies in the codebase.

Tools: Debugging tools integrated with AI-driven anomaly detection to identify and fix errors efficiently.

Example Prompt: Modify this code to remove the bugs:

for (let i = 0; i < cars.length; j++) {
	text += cars[i] + “<br>’;
}

AI

Response:

// Given array cars  
const cars = [“Toyota”, “Ford”, “BMW”, “Audi”];
let text = “”;
 
// Loop through the cars array using the correct index variable i
for (let i = 0; i < cars.length; i++) {
 // Concatenate each car name to the text variable with “<br>” for line breaks
 text += cars[i] + “<br>”;
}
 
// Display the concatenated text
console.log(text);

Here are the changes made to fix the bugs:

• Changed j++ to i++ in the loop declaration to properly increment the index variable i.
• Replaced the incorrect single quote at the end of “<br>’ with a double quote (“<br>”), ensuring proper string closure.

This code will loop through the cars array, concatenate each car name with an HTML line break (<br>), and store the result in the text variable.

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ALGORITHM OPTIMIZATION

Use AI to analyze algorithms and propose optimizations for efficiency.

Tools: Algorithm analysis frameworks or AI-based algorithm optimization tools to enhance code performance.

Example Prompt: Optimize this python my_sort algorithm:

import random
 
# Sorts array a[0..n-1] def my_sort(a):
def my_sort(a):
	n = len(a)  
	while (is_sorted(a) == False):
		shuffle(a)
		
# To check if array is sorted or not def is_sorted(a):
def is_sorted(a):
	n = len(a)  
	for i in range(0, n-1):
		if (a[i] > a[i+1] ):
			return False
	return True
 
# Generate a new permutation of the array def shuffle(a):
def shuffle(a):
	n = len(a) 
	for i in range (0,n):
		r = random.randint(0, n-1)
		a[i], a[r] = a[r], a[i]
 

AI

Response:

def my_sort(a):
 a.sort()

This simple change utilizes Python’s built-in sorting function, sort(), which uses the highly optimized Timsort algorithm under the hood. Timsort is a hybrid sorting algorithm derived from merge sort and insertion sort, designed to perform very well on many kinds of real-world data.

Example Follow-Up Prompt: What is the efficiency of the algorithm in Big O before and after the modification?

AI

Response:

• The original algorithm relies on repeatedly shuffling the array and checking if it’s sorted. This approach doesn’t have a deterministic time complexity but can be quite inefficient, especially for large arrays. The worst-case time complexity can be considered as unbounded due to the randomness of the shuffling process.

After the modification:

• Using the sorted() function or the sort() method, the time complexity is O(n log n) on average, where n is the number of elements in the array. Both sorted() and sort() use highly optimized sorting algorithms (like Timsort in CPython) which exhibit this time complexity.

In summary, before the modification, the algorithm’s time complexity was highly variable and potentially very inefficient. After the modification, the time complexity is O(n log n), providing a much more predictable and efficient sorting process for large arrays.