GitHub Copilot's capabilities are bound by its training data. This data, while vast, is limited to publicly available source code from a specific period, meaning it may lack context from private repositories or knowledge of the very latest libraries and language features. This is a foundational limitation.

A direct consequence of this training data composition is that Copilot's proficiency is not uniform across all programming languages. It performs best with languages that are highly represented in public repositories (e.g., JavaScript, Python), while its support and the quality of suggestions for less common or niche languages are significantly limited.

B. Ability to handle complex code structures: This is a known area where Copilot can be limited, but the statement is phrased as a capability, not a limitation. The limitations described in A and D are more fundamental.

C. No biases in code suggestions: This statement is factually incorrect. Like any model trained on large, public datasets, GitHub Copilot can reproduce biases, insecure coding patterns, and outdated practices present in its training data.

1. GitHub Docs

"Frequently asked questions about GitHub Copilot": This official documentation explicitly addresses the limitations stemming from the training data and language support.

Regarding training data (A): "The model that powers GitHub Copilot is trained on a large corpus of natural language text and source code from publicly available sources

including code in public repositories on GitHub." This confirms the data is finite and from a specific source.

Regarding language support (D): "For each language

the quality of suggestions you receive may depend on the volume and diversity of training data for that language... Languages with less representation in public repositories may be less supported." This directly contradicts the idea of extensive support for all languages.

Regarding bias (C): "The training data... may contain biases... GitHub Copilot may generate code that reflects these biases." This confirms that the statement "No biases" is false.

2. Chen

M.

et al. (2021). "Evaluating Large Language Models Trained on Code." arXiv preprint arXiv:2107.03374. This foundational paper on OpenAI Codex (the model behind Copilot) discusses the model's training on publicly available GitHub code and evaluates its performance

which inherently varies by task complexity and language. (Section 2.1

"Training Dataset"). DOI: https://doi.org/10.48550/arXiv.2107.03374