AI For Solving Coding Problems

Creating high quality code is important, but one of the biggest questions in the space right now is can AI be used to solve coding problems?

Over the last few years, there has been a large increase in the number of software developers using artificial intelligence (AI) to generate code and to solve coding problems. For programmers, or people with a general interest in coding, it can be daunting to know where to get started with code generation using AI tools. In this article, I will describe how anyone can get started quickly with an AI code assistant and begin to boost productivity in their software development process.

Why Use an AI Tool?

AI coding tools have taken the software industry by storm, however there are still a large number of developers who have not started to integrate with this technology. With tools such as Github Copilot and Amazon CodeWhisperer becoming available, it has never been easier to streamline the process of writing code using an AI powered code assistant, whilst still producing high quality code. Using AI to generate code is also relatively inexpensive, with many extremely affordable machine learning powered code tools available.

GitHub Copilot

GitHub Copilot is an advanced AI code completion tool designed to help developers write code by providing suggestions for whole lines or blocks of code. As a product developed by GitHub in collaboration with OpenAI, it integrates seamlessly with Visual Studio Code, one of the most popular code editors which is used to develop some of the world's largest projects. Copilot draws from a vast range of sources, including public code repositories on GitHub, to offer contextually relevant code suggestions, significantly speeding up the coding process and potentially reducing errors. It supports a multitude of programming languages and frameworks, making it a versatile tool for developers.

As for pricing, at the time of writing GitHub Copilot offers a paid three-tier subscription model consisting of:

• Copilot Individual ($10/month)
• Copilot Business ($19/month)
• Copilot Enterprise ($39/month)

The exact pricing may vary and should be checked on GitHub's official website for the most current information. Its compatibility with Visual Studio Code, combined with its intelligent code completion capabilities, makes it a valuable tool for developers looking to streamline their coding workflow.

Amazon CodeWhisperer

Amazon CodeWhisperer is a machine learning-powered coding assistant developed by Amazon Web Services (AWS). It's designed to improve the efficiency and productivity of developers by providing real-time code recommendations. One of its prominent features is the ability to suggest entire lines or blocks of code based on the context of the current project. This includes integrating with the developer's own codebase and understanding the nuances of their specific coding style.

Compatibility with various Integrated Development Environments (IDEs) is a key aspect of Amazon CodeWhisperer. It supports Visual Studio Code (VS Code), one of the most popular IDEs among developers. This allows a wide range of programmers to integrate CodeWhisperer seamlessly into their workflow.

Comparing Amazon CodeWhisperer with GitHub Copilot, there are some notable differences. While both tools offer code suggestion capabilities, GitHub Copilot is based on OpenAI's Codex model and has been noted for its ability to generate code in a wide range of programming languages and frameworks. Copilot also integrates tightly with GitHub's ecosystem, potentially offering more extensive support for GitHub-hosted projects. On the other hand, CodeWhisperer offers better integration with AWS services and tools, making it particularly beneficial for developers working extensively within the AWS ecosystem.

Foundation Models

Foundation models like ChatGPT and Gemini are revolutionizing how we approach coding problems, fundamentally changing our interactions with programming languages. These models are a subset of artificial intelligence that specialize in understanding and generating human-like text. In the realm of programming, they are particularly adept at code generation and debugging, offering an invaluable resource to both novice and experienced developers.

One of the primary advantages of using models like ChatGPT and Gemini (Bard) in coding is their ability to understand and write code in multiple languages. Whether it's Python, JavaScript, C++, or any other popular language, these models can generate functional code snippets based on user specifications. This flexibility is especially useful for developers who work across various programming platforms or for those learning new languages.

Moreover, these models are equipped with advanced natural language processing capabilities. This means they can interpret coding problems described in plain English and translate them into executable code. It's a significant leap forward, as it lowers the barrier to entry for coding and allows for a more intuitive problem-solving approach. Instead of poring over documentation or searching through forums, a developer can directly ask the model to write code that performs a specific function or solves a particular problem.

Another area where foundation models excel is in debugging code. By analyzing code snippets, they can identify syntax errors, logical mistakes, and other common bugs. They provide suggestions for corrections, often explaining why an error occurred and how to avoid similar issues in the future. This not only helps in fixing immediate problems but also aids in learning and improving coding skills.

However, it's important to note that while these models are powerful, they are not infallible. Their suggestions should be reviewed and tested, as they can occasionally generate incorrect or inefficient code. The human oversight remains a crucial aspect of the coding process.

The integration of foundation models like ChatGPT and Gemini into the coding world is a major step forward. Their ability to understand and generate code in multiple programming languages, combined with their natural language processing skills, makes them a valuable ally in writing and debugging code. They democratize access to programming, making it more accessible and less intimidating for a wider range of people. As these models continue to evolve, we can expect even more sophisticated and reliable coding assistance.