Copilot is appealing because it taps into a number of developer annoyances.
What is Copilot?
GitHub Copilot is an AI pair programmer which suggests line completions and entire function bodies as you type. GitHub Copilot is powered by the OpenAI Codex AI system, trained on public Internet text and billions of lines of code. It’s a strong source code analyzer that supports a wide range of programming languages.
The purpose of OpenAICodex is to learn “how” people utilize code. It determines the context of the code you’re writing and provides suggestions for what might happen next. Unlike autocomplete in an IDE, Copilot can generate fresh output from the code it has learned. It’s not just a list of code you’ve seen before.
How far has it reached?
“Not very far,” is the overall current response. Despite all of the buzzwords like “intelligence,” “contextual,” and “synthesizer,” Copilot has only a limited understanding of your genuine aims and what your code needs to accomplish.
When computing suggestions, Copilot simply looks at your current file. It will not evaluate how the code is used throughout your program. Even if the underlying thinking behind the files remains the same, the AI’s view of your work may differ dramatically from yours and may vary file-by-file.
What are its Limitations and Problems? Why won’t it Reach its Ending?
Copilot is appealing because it taps into a number of developer annoyances. Most programmers, if not all, recognize the inefficiency of writing “boilerplate” code that isn’t really relevant to their project. If you take Copilot at face value, you’ll see that now they have a solution that allows them to focus more on the creative aspects of their business.
Copilot has been trained on a range of public GitHub projects with various licenses. This, according to GitHub, constitutes “fair use” of such projects.
This is when the issue starts. Copilot still has a chance of accurately reproducing code portions. This could land your project in hot trouble, depending on the permissions around those bits. Because Copilot was trained on GitHub projects, personal data may be introduced into your source files. These are supposed to be unusual occurrences. If the surrounding code context is weak or imprecise, they’re believed to be more likely. Because derivative works under the GPL and other comparable licenses, incorporating the same permissions, putting GPL code into a commercial product is a licensing violation. Therefore, using Copilot has major legal implications that you should consider before installing it. Accepting a suggestion could result in unintentional copyright infringement because Copilot appears to emit code verbatim without disclosing the license that comes with the sample.
This should prove beyond a shadow of a doubt that Copilot’s initial release will not be able to take the position of a human developer. Its code isn’t guaranteed to be relevant, and it could be defective or old, posing a legal danger.
The problem with Copilot is GitHub’s blanket approach to training the model. Copilot’s real-world use will be hampered by the inclusion of GPL-licensed code and the complete lack of any type of output testing. It’s unclear whether GitHub’s decision to train the model on public code qualifies as fair use, it’s possible that it doesn’t, at least in some places.
Furthermore, because GitHub is unable to ensure that Copilot code actually works, developers will need to proceed with caution and evaluate everything it creates. Copilot’s promise includes assisting inexperienced developers in progressing, but this will not be possible if potentially flawed code is suggested and accepted.
Finally, Copilot gives no explanation of how or why its recommendations operate. If technology is to really replace human developers, it must be able to explain how a solution works and provide transparency into the decisions that were made. Developers can’t just trust the machine; they’ll need to keep an eye on it and compare alternative options.
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