GitHub Co-Pilot: Feature or Threat?

In order to help Visual Studio Code users, Github and OpenAI created the artificial intelligence programme GitHub Copilot.

GitHub made the first announcement on June 29, 2021.

In order to generate legitimate computer code, GitHub Copilot employs a modified version of GPT-3, a language prediction model that was intended to generate text that resembles human speech.

The public GitHub repositories of any licence are used for Copilot’s training.

The Benefits of Copilot

Of course, it goes without saying that, throughout time, robots have greatly facilitated the work of their human counterparts, and the Github co-pilot is no exception.

Who wouldn’t want great capabilities like code auto-generation and completion while working, after all?

But how exactly does this operate?

How can we achieve code auto-generation?

OpenAI Codex, a GPT-3 ancestor that was trained on publicly accessible source code and spoken language, is capable of understanding both programming languages and spoken languages.

Using OpenAI Codex to synthesize and recommend individual lines of code and whole functions, the GitHub Copilot editor plugin transmits your comments and source code to the GitHub Copilot service.

Developers may now quickly and simply auto-generate functions from a vast pool of current code repositories, saving firms a tonne of money on production costs thanks to the copilot’s excellent and faultless capability.

The Copilot’s Drawbacks

The GitHub co-pilot has won many awards for efficiency, but there are still enough flaws in the way that it creates code that compromise its ability to write clean code.

The code GitHub Copilot offers might not always function or even make sense, but it strives to comprehend your meaning and provide the best code it can.

While we are working hard to improve GitHub Copilot, any code that is proposed by GitHub Copilot should be thoroughly tested, evaluated, and verified.

You are in complete control as the developer.

The concept that the codes must be reviewed and tested adds another layer of time complexity since more time must be spent on trying to rework the code

Another flaw with the copilot is that it still struggles to speak most other languages; instead, according to Github, “its strength is in such languages as Python, JavaScript, TypeScript, Ruby, and GO.”

The following are some other copilot drawbacks:

sluggish code execution

Interrupting the flow.

Working on projects could become interrupted if Github Copilot’s code recommendations need to be reviewed.

The possibility that reliance might develop over time is a worry.

Many would develop addictions and depend on its support.

The copilot could provide programming suggestions that you don’t comprehend.

There is debate about the copilot’s training program’s legality as well because it is believed that doing so automatically violates copyrights.

Are software developers going to be replaced by Github Copilot?

My opinion on this is that for the time being, I do not see this AI displacing developers, at least not in the very near future, and perhaps soon enough this once-dreaded piece of tool will unravel a future where software development would be more faster, more effective, and frictionless for developers.

With the invention of tools like code editors and debugging tools, we have witnessed a gradual improvement in the experience of developers.