Unveiling the Remarkable Potential of Generative AI in Software Development

7 min readSep 4, 2023

Software development is amongst the most complicated jobs available in the world. Also in terms of scale, if not months, it at least takes a couple of weeks to develop even a simple software with a proper team.

With numerous programming languages, frameworks, complex requirements, fast-moving trends, and technologies, it is often difficult to comprehend a software project. In fact, even the top software development companies sometimes take years to complete a project. In such circumstances, having access to a technology like Generative AI is a blessing.

Generative AI can not only speed up the entire development process but can also provide active support in terms of testing and maintenance. Therefore to carefully peel the layers of understanding the potential of Generative AI in software development, read this article further ahead…

Generative AI in Software Development — Generative AI Features!

The usage of Generative AI in the domain of software development is empirical. To show Generative AI’s remarkable capabilities, we have mentioned the key areas where tools like ChatGPT and Google Bard can make a significant contribution:

Code Generation: Based on the accuracy of the prompt, generative AI can generate code snippets, functions, and sometimes even entire programs. Everything depends on the description and the requirements of the user
Code Completion and Suggestions: Get real-time suggestions with code editors for code lines, variable names, and function calls
Bug Detection and Correction: Generative AI can detect bugs and provide errors by detecting patterns and looking for anomalies & mistakes in the code
Code Refactoring: Generative AI helps the user make small and subtle changes in the code that optimize the snippet
Natural Language Conversion: The user does require some domain knowledge, however, generative AI can correct natural human language to code snippets. The results depend on the accuracy of the prompt
Documentation: An important aspect of software development is documentation which can be created with the assistance of generative AI. It can be done for code, APIs, and libraries
Code Summarization: Generative AI can create summaries for code making it easier for developers to understand a particular snippet
Code Synthesis from Pseudocode: Generative AI can produce code using high-level pseudo code provided by the developer

Generative AI and Software Development — Ideal Platform for Development Assistance!

Generative AI is an all-purpose technology with tools that can help its users with “N” number of chores. It can be an English teacher, it can help learn different concepts, learn different languages, assist in writing, and much more.

It is the ultimate assistant in its true sense. These capabilities of Generative AI also dominate the realm of software development. An operation that is complex and can be eased out with support from a Generative AI tool.

Below are some of the generative AI Tools that are at par in terms of potential use of generative AI:

ChatGPT Plus: The tool offers a variety of features that enable it with capabilities to help in the process of software development. Some features are natural language understanding, answering questions, creating code snippets, bug detection, etc.
Google Bard: In case you need assistance with code generation or debugging, Google Bard is generative artificial intelligence that is capable of doing it. In fact, it can debug its own code in case of an issue
Github Copilot: It’s not the most influential generative artificial intelligence. However, Github’s co-pilot is capable of completing code with intelligent suggestions.

How to Enable Development Environment in Generative AI?

In order to take full advantage of the potential of generative AI, we can use it for a host of things. One such thing is the setup of the development environment. Here, we’ll be explaining things from the guise of setting up Python on the user’s system via ChatGPT.

Below are the steps to install Python on the user’s system as a response from ChatGPT’s generative AI:

By using these steps, anyone can unveil the potential of generative AI for installing an environment for any language or framework.

How to use Generative AI to Write Code?

To further use generative AI in software development, we can take active support from generative AI to do so. Here again, we’ll be taking the example of ChatGPT considering it is the most popular generative AI.

Therefore without any further ado, let’s begin:

Step 1: Choose a Programming Language of your Choice

ChatGPT is capable of generating codes in multiple languages such as C++, Python, Java, Javascript, etc. Therefore, it is important to be specific when it comes to writing a particular code.

Step 2: An Accurate Prompt

While using ChatGPT for generating code, it is important to specify each and every detail imagining a dolt. ChatGPT is an application and doesn’t get the context by itself. It is the job of the user to specify it. So for instance, if you want a code snippet for bubble sort simply write “Code in Python for Bubble Sort”.

Step 3: Click on Generate

Once, you are done specifying the AI prompt, it’s time to generate the code.

Below is the code snippet generated by writing the prompt mentioned in the second step:

def bubble_sort(arr):
n = len(arr)
for i in range(n):
# Last i elements are already in place, no need to check them
for j in range(0, n — i — 1):
# Swap if the element found is greater than the next element
if arr[j] > arr[j + 1]:
arr[j], arr[j + 1] = arr[j + 1], arr[j]

# Example usage
if __name__ == “__main__”:
input_list = [64, 34, 25, 12, 22, 11, 90]

print(“Original List:”, input_list)
bubble_sort(input_list)
print(“Sorted List:”, input_list)

Similarly, any user can take the help of generative AI to generate code pieces. However, it is important for the user to be incredibly precise to get a code snippet that is usable.

Advantages of Using Generative AI for Software Development

To truly understand the benefit of using the combination of Generative AI and software development. Here are some points that will help you consider Generative AI in software development lifecycle.

Here they are:

Accelerated code production taking up manual coding workload and fastening SDLC (software development life cycle)
Faster production of prototypes to visualize the software
Enhanced capabilities to detect bugs and vulnerabilities in the code
Ease of code generation for people with a non-tech background with surface-level domain knowledge
Enables comprehensive testing for its user
Capable of providing a personalized experience for code generation
Propose solutions for problems in coding that exists
Capable of automating the CI/CD pipeline

Future of Generative AI for Software Development

The future of generative AI for software development will be the prominence of this methodology. There have been numerous report that explains the power of generative AI and the results it has produced for software development.

The graph by McKinsey clearly states that there is a huge difference in the time taken for software development with or without generative artificial intelligence. The technology will be catapulting the speed of the process and will enable developers to take up much more complicated tasks. With generative AI in software testing and software development life cycle creating softwares with all shapes and sizes will become easy. Adding to it, there is a high possibility that non-tech people might also start learning using the tool and create their own softwares using tools like ChatGPT, Bard, Github Co-pilot, etc.

Wrapping Up!

If not for the century, Generative artificial intelligence will be the innovation of the decade. It is a compelling technology that not only supports coders but also artists, content writers, researchers, doctors, and many other people with different use cases. Generative AI tools are the door to finding answers to almost every query. There are numerous artificial intelligence development companies are constantly using LLM (large language model) to adopt it for creating generative AI solutions for different industries and niches. Also with the exceptional capabilities of the technology, it’ll soon become an active part of solving every problem statement.

FAQs:

Q.1 What are some generative AI examples?

A.1 Some generative AI examples are:

Google Bard (for generating text-based content)
ChatGPT (for generating text-based content)
Dall-E2 (for creating images)
DeepMind’s Generative Design (for creating designs for products and systems)

Q.2 Explain different generative AI models?

A.2 There are several generative AI models. Let’s check out the most common ones:

Generative Adversarial Networks (GANs)
Variational Autoencoders (VAEs)
Diffusion Models
Autoregressive Models
Flow-based Models

Q.3 What are some generative AI trends?

A.3 Here are some of the generative AI trends for you to check out in 2023:

Improvement in natural language processing models because of accessibility to large amounts of data
Generation of audio, images, videos, and 3D models using generative AI
Creation of complete videos using generative that are personalized in nature

Q.4 What are some generative AI companies?

A.4 There are several generative AI companies that back generative AI projects are:

OpenAI
Google
Microsoft Research
DeepMind
Generative AI Labs