Complete code and assist the development process
Azure OpenAI can assist developers in common software development tasks such as writing unit tests, completing partial code, commenting code, and generating documentation. Using AI assistance can enable developers to spend more time on complex programming and problem solving tasks.
Complete partial code
Azure OpenAI models are capable of generating code based on comments, function names, and partially written code. The more context you can provide the model, the more accurate the response likely is.
For example, when given the following prompt:
complete the following function
"""
# calculate the average of the numbers in an array, but only if they're even
def
complete the following function
"""
// calculate the average of the numbers in an array, but only if they're even
public
The model takes the comments and start of the function definition, and complete it from whatever context it has.
# calculate the average of the even numbers in an array
def calculate_even_average(numbers):
total = 0
count = 0
for number in numbers:
if number % 2 == 0:
total += number
count += 1
if count == 0:
return 0 # avoid division by zero
else:
return total / count
// calculate the average of the even numbers in an array
public double CalculateEvenAverage(int[] numbers) {
int total = 0;
int count = 0;
foreach (int number in numbers) {
if (number % 2 == 0) {
total += number;
count++;
}
}
if (count == 0) {
return 0; // avoid division by zero
} else {
return (double) total / count;
}
}
In this case, it was able to discern what we wanted due to a relatively simple and complete comment preceding the start of the function. With more complex functions or tasks, more context is required to get a useful response.
It's important to note that for some languages, like Python, the syntax is clear enough for the model to assume what language is desired. For syntactically similar languages like C# or Java, there's a higher likelihood that the model uses the wrong language. If that is the case, try specifying the language in the prompt or providing more code for context.
Similar functionality can be seen by providing partial code to the model, without any comments. Take the following prompt for example.
complete the following code
"""
def func1(n)
if n==0:
complete the following code
"""
public int Func1(int n) {
if (n == 0) {
The AI model does its best to complete the function with the most likely completion of the function.
def func1(n):
if n == 0:
return 1
else:
return n * func1(n-1)
public int Func1(int n) {
if (n == 0) {
return 1;
} else {
return n * Func1(n-1);
}
}
The response likely also includes a natural language explanation of what that function is doing. In this case, it responded with a recursive implementation of the factorial function. However, the prompt could have also been the start of a Fibonacci function, which illustrates a case where we need more context in the prompt to better indicate what we want from the model.
Tools such as GitHub Copilot utilize OpenAI models to suggest code and function completions in a similar way to the above examples. GitHub Copilot is an excellent example of real-world usage of this functionality that uses built-in prompts and the context of the code you're writing to act as your paired programmer.
Write unit tests
Azure OpenAI models can generate unit tests for functions you write to help make your code more robust. Take for example the binary search function.
def binary_search(arr, target):
left, right = 0, len(arr) - 1
while left <= right:
mid = (left + right) // 2
if arr[mid] == target:
return mid
elif arr[mid] < target:
left = mid + 1
else:
right = mid - 1
return -1
public static int BinarySearch(int[] arr, int target)
{
int left = 0;
int right = arr.Length - 1;
while (left <= right)
{
int mid = (left + right) / 2;
if (arr[mid] == target)
{
return mid;
}
else if (arr[mid] < target)
{
left = mid + 1;
}
else
{
right = mid - 1;
}
}
return -1;
}
Provide that function along with the prompt write three unit tests for this function, and you get a response similar to the following.
def test_binary_search():
# Test case 1: Target element is in the middle of the array
arr1 = [1, 2, 3, 4, 5, 6]
target1 = 4
assert binary_search(arr1, target1) == 3
# Test case 2: Target element is at the beginning of the array
arr2 = [1, 2, 3, 4, 5, 6]
target2 = 1
assert binary_search(arr2, target2) == 0
# Test case 3: Target element is not in the array
arr3 = [1, 2, 3, 4, 5, 6]
target3 = 7
assert binary_search(arr3, target3) == -1
[Test]
public void TestBinarySearch1()
{
int[] arr = { 1, 2, 3, 4, 5 };
int target = 3;
int expected = 2;
int result = BinarySearch(arr, target);
Assert.AreEqual(expected, result);
}
[Test]
public void TestBinarySearch2()
{
int[] arr = { 10, 20, 30, 40, 50 };
int target = 35;
int expected = -1;
int result = BinarySearch(arr, target);
Assert.AreEqual(expected, result);
}
[Test]
public void TestBinarySearch3()
{
int[] arr = { 3, 6, 9, 12, 15 };
int target = 6;
int expected = 1;
int result = BinarySearch(arr, target);
Assert.AreEqual(expected, result);
}
This functionality is even more useful if you specify the cases you want included in your prompt, which reduces the time it takes for you to write unit tests for your code.
Add comments and generate documentation
To further improve your code, AI models can add comments and documentation for you. Take the following function as an example, which can be a little hard to understand when first reading it without any code comments.
def permutations(lst):
if len(lst) == 0:
return []
elif len(lst) == 1:
return [lst]
else:
result = []
for i in range(len(lst)):
temp = lst[i]
remaining = lst[:i] + lst[i+1:]
for p in permutations(remaining):
result.append([temp] + p)
return result
public static List<List<int>> Permutations(List<int> lst)
{
if (lst.Count == 0)
{
return new List<List<int>>();
}
else if (lst.Count == 1)
{
return new List<List<int>> { lst };
}
else
{
List<List<int>> result = new List<List<int>>();
for (int i = 0; i < lst.Count; i++)
{
int temp = lst[i];
List<int> remaining = new List<int>(lst);
remaining.RemoveAt(i);
foreach (List<int> p in Permutations(remaining))
{
List<int> permutation = new List<int> { temp };
permutation.AddRange(p);
result.Add(permutation);
}
}
return result;
}
}
Provide that function to the model, along with a prompt requesting that it add comments to the code, and you get a response similar to the following.
def permutations(lst):
# Check if the input list is empty
if len(lst) == 0:
return []
# Check if the input list has only one element
elif len(lst) == 1:
return [lst]
else:
# Initialize an empty list to store permutations
result = []
# Iterate through each element of the input list
for i in range(len(lst)):
# Remove the current element from the list and store it in a variable
temp = lst[i]
remaining = lst[:i] + lst[i+1:]
# Recursively call the function on the remaining list
for p in permutations(remaining):
# Append the current element to the beginning of each permutation and add it to the result list
result.append([temp] + p)
# Return the final list of permutations
return result
public static List<List<int>> Permutations(List<int> lst)
{
// Check if the input list is empty
if (lst.Count == 0)
{
return new List<List<int>>();
}
// Check if the input list has only one element
else if (lst.Count == 1)
{
return new List<List<int>> { lst };
}
else
{
// Initialize an empty list to store permutations
List<List<int>> result = new List<List<int>>();
// Iterate through each element of the input list
for (int i = 0; i < lst.Count; i++)
{
// Remove the current element from the list and store it in a variable
int temp = lst[i];
List<int> remaining = new List<int>(lst);
remaining.RemoveAt(i);
// Recursively call the function on the remaining list
foreach (List<int> p in Permutations(remaining))
{
// Append the current element to the beginning of each permutation and add it to the result list
List<int> permutation = new List<int> { temp };
permutation.AddRange(p);
result.Add(permutation);
}
}
// Return the final list of permutations
return result;
}
}
Take it one step further, and request documentation for the function it just added comments to.
def permutations(lst):
"""
Generates all possible permutations of a given list of integers using recursion.
Args:
lst (list): The list of integers to generate permutations for.
Returns:
list: A list of all possible permutations of the input list.
"""
# Check if the input list is empty
if len(lst) == 0:
return []
# Check if the input list has only one element
elif len(lst) == 1:
return [lst]
else:
# Initialize an empty list to store permutations
result = []
# Iterate through each element of the input list
for i in range(len(lst)):
# Remove the current element from the list and store it in a variable
temp = lst[i]
remaining = lst[:i] + lst[i+1:]
# Recursively call the function on the remaining list
for p in permutations(remaining):
# Append the current element to the beginning of each permutation and add it to the result list
result.append([temp] + p)
# Return the final list of permutations
return result
/// <summary>
/// Generates all possible permutations of a given list of integers using recursion.
/// </summary>
/// <param name="lst">The list of integers to generate permutations for.</param>
/// <returns>A list of all possible permutations of the input list.</returns> ```
public static List<List<int>> Permutations(List<int> lst)
{
// Check if the input list is empty
if (lst.Count == 0)
{
return new List<List<int>>();
}
// Check if the input list has only one element
else if (lst.Count == 1)
{
return new List<List<int>> { lst };
}
else
{
// Initialize an empty list to store permutations
List<List<int>> result = new List<List<int>>();
// Iterate through each element of the input list
for (int i = 0; i < lst.Count; i++)
{
// Remove the current element from the list and store it in a variable
int temp = lst[i];
List<int> remaining = new List<int>(lst);
remaining.RemoveAt(i);
// Recursively call the function on the remaining list
foreach (List<int> p in Permutations(remaining))
{
// Append the current element to the beginning of each permutation and add it to the result list
List<int> permutation = new List<int> { temp };
permutation.AddRange(p);
result.Add(permutation);
}
}
// Return the final list of permutations
return result;
}
}