Generative AI Coding: Top 10+ Use Cases & 5 Tools in 2024

Software development tends to take longer and cost more than expected. Generative AI is the latest addition to approaches and technologies to speed up the development time, especially for software developers.

Though generative AI tools have the potential to accelerate the software development process, they come with challenges such as hallucination which can slow down coding or introduce critical issues to software. This is particularly relevant when working with large language models in code development.

To help technical leaders form their generative AI approach to software engineering, we will:

• Explain the top 3 use case areas of generative AI in coding
• Highlight 4 generative AI coding tools
• Provide best practices

Generative AI in Requirements Analysis

Without clear requirements, the software development process cannot start. Generative AI applications in requirements analysis include:

1- Generating requirements

Analysts can prompt their high level requirements to fine-tuned large language models that have been exposed to best practice requirements documents.

2- Requirement completion

Analysts may forget important requirements (e.g. regarding security) that can be completed.

Generative AI in Coding

3- Code generation

Writing code with generative AI is possible through a technique known as neural code generation. This involves training a neural network on a large dataset of computer code examples and then using the fine tuned network to generate code that is similar in structure and function to the examples it has been trained on.

Figure 1. Generating a simple Python script with OpenAI’s ChatGPT¹

4- Code completion 

One of the most straightforward uses of generative AI for coding is to suggest code completions as developers type. This can save time and reduce errors, especially for repetitive or tedious tasks.

5- Code review 

Generative artificial intelligence can also be used to make the quality checks of the existing code and optimize it either by suggesting improvements or by generating alternative implementations that are more efficient or easier to read.

Figure 2. The mechanism of Amazon’s CodeWhisperer for reviewing code

Source: Amazon

6- Bug fixing

It can help identify and fix bugs in the generated code by analyzing code patterns, identifying potential problems, and suggesting fixes.

Figure 3. ChatGPT is fixing a Python code that tries to calculate factorial of a number but runs into an error

7- Code refactoring

It can be used to automate the process of refactoring code, making it easier to maintain and update over time.

8- Style improvement

It can analyze code for adherence to coding style guidelines, ensuring consistency and readability across a codebase.

Generative AI in Testing

Testing is critical in software and generative AI can support testing in a similar way to its support for software coding. Generative AI applications in test automation and testing include:

• Generating test cases
• Generating test code
• Test script maintenance
• Test data generation
• Test documentation
• Test result analysis

Generative AI Coding Tools for Software Development

Solutions are sorted by their introduction date, starting from the first introduced solution:

1- GitHub Copilot

GitHub Copilot, Microsoft’s AI system for writing code, is an AI-powered code suggestion and generation tool developed in collaboration with OpenAI.² It uses machine learning models trained on a vast corpus of public code to suggest code snippets and even entire functions as developers type.

 Figure 4. Survey responses measuring dimensions of developer productivity when using GitHub Copilot

Source: GitHub Blog

According to executives at GitHub and other companies, the aim of these tools is not to substitute developers but to enhance their efficiency, similar to the way spell check and phrase auto-completion tools help people in writing documents.³ 

The results of it can be seen in the survey done with developers (see Figure 3). These tools suggest fresh code snippets and tests, and give technical advice within the existing code-writing programs that developers use.

2- ChatGPT

As an AI model developed by OpenAI, ChatGPT does not have a specific code generation function. However, as a language model trained on a large corpus of text data, it can generate text in natural language, which includes code snippets or examples, when prompted to do so.

For example, if a user asks “Can you provide an example of a Python function that calculates the sum of two numbers?” ChatGPT can generate a code snippet in response, such as in Figure 5.

Figure 5. An example of ChatGPT generating code for the given prompt and explaining it

While ChatGPT is not specifically designed for generating code, it can be trained on a dataset of code examples to improve its ability to generate code snippets and functions that are syntactically correct and functionally valid. However, it’s important to note that the quality of the generated code may vary depending on the quality and quantity of the training data and the complexity of the task being performed.

3- CodeWhisperer

Amazon’s CodeWhisperer is a code generation tool that utilizes diverse data sources, including Amazon.com and open-source code, to produce code that imitates the way a developer would write it.⁴ 

It comprehends comments expressed in natural language, creates code based on the developer’s objectives, and corresponds to the developer’s style and patterns. Additionally, while typing, CodeWhisperer offers suggestions to complete the comment. Users have the option to accept the top suggestion, view additional recommendations, or proceed with writing their own code.

4- IBM Watson Code Assistant

Though not yet released to the public, Watson Code Assistant relies on IBM watsonx.ai foundation models to generate code.

For other code generator tools and information about their features and pricing, you can check our generative AI tools article.

5- Code Llama

Llama 2 was finetuned by Meta for coding and comes in 3 flavors:⁵

• Code Llama for various programming languages
• Code Llama – Python for Python.
• Code Llama – Instruct for improved comprehension of natural language instructions.

Best practices for software development with generative AI

1- Address Intellectual Property (IP) concerns

For IP of code generated by LLMs to be deployed in a commercial software, these are the minimum requirements from a generative AI IP perspective:

• Its codebase needs to be licensed to the end user (potentially via the developer of the LLM). There is an ongoing lawsuit about the use open source code in Github’s Copilot.

2- Ensure data security of code

Enterprises have already leaked valuable source data while using generative AI applications.⁶ Ensuring security of source code is critical.

3- Follow software development best practices

Like humans, software makes mistakes. Software engineering discipline has produced hundreds of paradigms like pair programming to improve the quality and effectiveness of software. They can help minimize the downsides of generative-AI-powered software development.

For more, check out AIMultiple’s guidelines for enterprise generative AI models.

Coding is one of many generative AI applications. For applications in other industries and business functions, check out:

• Education
• Fashion
• Banking
• Marketing

If you have questions or need help in finding vendors, reach out:

External Links

External Links

• 1. “Introducing ChatGPT.” OpenAI, 30 November 2022. Accessed 2 January 2024.
• 2. “GitHub Copilot · Your AI pair programmer · GitHub.” GitHub. Accessed 2 January 2024.
• 3. “Generative AI Helping Boost Productivity of Some Software Developers.” The Wall Street Journal, 21 February 2023. Accessed 5 March 2023.
• 4. “AI Code Generator – Amazon CodeWhisperer – AWS.” AWS. Accessed 2 January 2024.
• 5. “Introducing Code Llama, a state-of-the-art large language model for coding“, Meta, August 24, 2023, Retrieved October 1, 2023.
• 6. “Samsung Bans Staff’s AI Use After Spotting ChatGPT Data Leak”. Bloomberg. May 2, 2023. Accessed May 28, 2023