AI-powered code assistants are widely used to generate code completions, significantly boosting developer productivity. However, these tools typically present suggestions without explaining their rationale, leaving their decision-making process inscrutable. This opacity hinders developers' ability to critically evaluate outputs, form accurate mental models, and calibrate trust in the system. To address this, we introduce CopilotLens, a novel interactive framework that reframes code completion from a simple suggestion into a transparent, explainable interaction. CopilotLens operates as an explanation layer that reconstructs the AI agent's "thought process" through a dynamic, two-level interface. The tool aims to surface both high-level code changes and the specific codebase context influences. This paper presents the design and rationale of CopilotLens, offering a concrete framework and articulating expectations on deepening comprehension and calibrated trust, which we plan to evaluate in subsequent work.
@article{arxiv.2506.20062,
title = {Beyond Autocomplete: Designing CopilotLens Towards Transparent and Explainable AI Coding Agents},
author = {Runlong Ye and Zeling Zhang and Boushra Almazroua and Michael Liut},
journal= {arXiv preprint arXiv:2506.20062},
year = {2025}
}
Comments
accepted at The First Workshop on the Application of LLM Explainability to Reasoning and Planning (XLLM-Reason-Plan) @ COLM 2025