ChatGPT can generate code samples, but it's not intended to help coders become more proficient. Since OpenAI published the chatbot, more than one million users have registered for the free research preview of ChatGPT. The bot can create straightforward websites and programmes using JavaScript, Python, and React. It can also help develop new programming languages and detect problems in existing programmes.
Though ChatGPT can't currently create complex code, such as that needed for banking apps, it will master coding within the next ten years. However, it raises the question of whether ChatGPT will replace writers and developers like computers and robots have replaced cashiers and assembly-line workers, and eventually, taxi drivers.
Overreliance On ChatGPT
Overdependence on ChatGPT is already causing issues for the developer community. For example, Stack Overflow, a question-and-answer website used by programmers to write and troubleshoot code, briefly restricted the usage of ChatGPT, stating that it could not keep up with quality check requests that people perform on the website. In addition, creating high-quality software by hastily putting together programmes using pre-existing code is impossible.
Furthermore, there are intellectual property law concerns with pre-trained AI code production because of its inability to tell the difference between codes licensed under a restrictive or open license. If the AI uses a line of code from a library protected by intellectual property laws, it may put users in danger of not having the proper licenses to use that code. A similar issue has already resulted in a class action lawsuit against a different OpenAI-based tool, GitHub Copilot.
AI Not Dependable, Yet
Although relying on AI in software development is probably not the best idea, it is clear that we need human developers to create the software that people use every day. AI could be utilised as a programming tool to handle lower-level jobs, much like automation is used by security operations centres for scanning, monitoring, and primary incident response.
This is already happening. As part of GitHub's Copilot, programmers can use ChatGPT to fix problems, add tests, and make other enhancements to their code. In addition, CodeWhisperer is one of Amazon's machine language-powered tools, and it is meant to boost developers' output by making suggestions for improvement to their code based on comments and snippets of conversation they find in the integrated environment's natural language. A plugin for Visual Studio Code that integrates with ChatGPT has also been developed.
How ChatGPT Helps Programmers
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Put it to work in the quest for bug-free code. ChatGPT can be used to get help in debugging code by posting a snippet of code along with details about what should happen and what instead happens. -
Assist developers in finding out where their code is failing. -
As a source of feedback. ChatGPT won't provide you with any out-of-the-box suggestions, but it can show where your product falls short compared to the competition. -
Creating test scenarios. ChatGPT is well-versed in building unit tests.
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Coders using ChatGPT to learn how to code could be setting themselves up for a big fall. A correct result from running the resulting code cannot be assumed. It may work, but even if it does, it is not the best way to write code. -
ChatGPT can't give the complete codebase or product, thus if you don't have a clear strategy, it won't help.
A coder is someone who is able to structure a programme. Nonetheless, ChatGPT may pave the door for new job designations. Of course, ChatGPT won't change the world of technology or software development anytime soon. But the fear of robots taking over programming jobs is overblown. Developers with human cognition will always have a leg up on machine competence.
Hence, the future of programming is AI-assisted programming, whether we like it or not. Learning to use these potent tools is crucial, but it is equally important to guard against complacency. We have to use it with care.
Furthermore, in the AI future, for example, prompt engineering – a natural language processing concept of converting tasks into prompt-based datasets and then training a language model – will be in high demand. To get the most significant and best outcomes from chatbots, prompt engineers will be needed to grasp ideas and convert them into procedures for writing model inputs, which the AI will then use to provide better answers than a run-of-the-mill question.
Nivash Jeevanandam writes stories about the AI landscape in India and around the world, with a focus on the long-term impact on individuals and society. Views are personal and do not represent the stand of this publication.
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