Beyond Autocomplete: Designing CopilotLens Towards Transparent and Explainable AI Coding Agents

Transparency gaps in AI coding tools are a real problem: Current AI code assistants like GitHub Copilot provide suggestions without explanation, making it difficult for developers to understand why the AI made specific recommendations or to critically evaluate whether the code is actually appropriate for their use case.

Lack of explainability undermines trust calibration: When developers can't see the AI's reasoning, they struggle to form accurate mental models of the tool's capabilities and limitations, which can lead to either over-trusting flawed suggestions or under-trusting helpful ones—both problematic outcomes.

CopilotLens demonstrates a practical solution through layered explanations: The proposed framework addresses opacity by adding an explanation layer that shows developers both the high-level code changes being suggested and the specific codebase context that influenced the AI's decision, making the "thought process" visible and reconstructible.

Explainability is essential for responsible AI deployment: This work highlights that responsible AI in high-stakes domains like software development requires more than just accurate predictions—it requires developers to understand how and why the system reached its conclusions so they can maintain appropriate oversight and catch potential errors.

This is a design-forward approach to AI safety: Rather than treating explainability as an afterthought, CopilotLens embeds transparency into the user interface itself, suggesting that responsible AI requires thoughtful interaction design, not just technical fixes.