Be it software development or web scripting, the world of coding is rapidly evolving. And with the introduction of generative AI, various AI tools have become the developer’s best companions. AI for software developers has the potential to revolutionize how they perform their day-to-day tasks as it offers them unique functionalities like code completion, snippet generation, and even assistance in solving problems.
According to Grand View Research, the global AI-powered coding market is expected to reach $15.47 billion by 2030 growing at a CAGR of 38.5%.
The two prominent players in this industry are GitHub Copilot and ChatGPT. Further in this article, we will explore their strengths and weaknesses and guide you in choosing the best companion for you.
GitHub Copilot
Developed by GitHub and Microsoft, in association with OpenAI, GitHub Copilot is an incredible AI tool designed specifically for software developers and programmers. It can easily integrate with various IDEs (Integrated Development Environments) to offer real-time code suggestions and completions as you type.
It has been built on top of OpenAI’s Codex which is a large language model trained on a huge dataset of public code repositories available on GitHub. So, Copilot can easily learn the programming language you are using and understand the context of your code to generate relevant suggestions.
Using GitHub Copilot helps software developers boost their productivity. It writes repetitive code snippets within seconds, takes different approaches to solving a problem, and maintains consistency in code style throughout the project. Thus, AI professionals or developers can focus on more complex tasks and architectural designs.
ChatGPT
ChatGPT is currently the most popular conversational AI tool developed by OpenAI. It is a large language model that has a wide range of applications such as writing content, summarizing texts, translating languages, etc., and not just writing codes. Having a seamless conversational interface, helps users chat with it like how they do with normal human beings. It can be used to generate poems, scripts, and codes, with just a single prompt. Though it is not specifically designed for coding, it can do this job efficiently.
GitHub Copilot is trained primarily on code repositories. But ChatGPT is trained on huge datasets of codes and texts. So, it offers more features and more generalized problem-solving assistance. It can explain complex codes and their concepts in user-friendly language. Also, it can generate various creative code solutions. The accuracy and context-awareness of ChatGPT is not as fine-tuned for specific coding as it is in GitHub’s Copilot, but it is highly versatile and can be beneficial in brainstorming and solving problems in different ways.
Learn using CoPilot and ChatGPT through top AI certification courses and enhance your skills to generate flawless codes with these tools.
Comparison between CoPilot and ChatGPT
Which one should you go for?
Now, which one is the best AI tool for software developers? Well, it all ultimately depends on your specific coding needs and preferences, along with your technical familiarity. If you want to increase your productivity, want your code completed automatically, and ensure you adhere to best practices, then GitHub Copilot can prove to be a better choice.
But when it comes to versatility, general problem-solving, and exploring creative codes, then ChatGPT will be an ideal choice. But before you finally opt for any of these, why not try both tools, experience their strengths and weaknesses, and then come to a conclusion?
Conclusion
AI for software developers has seen quite a lot of transformation in the past years. The live example of GitHub CoPilot and ChatGPT signifies the shift in the way software developers and programmers approach coding. As we move towards the future, we can witness these tools offering more possibilities to streamline software development workflow. It will not only enhance creativity but will significantly reduce the entry barrier in the world of coding. It must also be noted that these tools are designed to augment the coding process and not replace human programmers. Therefore, they must also verify the relevancy of the code generated before implementing it on their projects.