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

TL;DR

This paper introduces CopilotLens, an interactive framework that enhances AI coding assistants by providing transparent explanations of their decision-making process, aiming to improve developer understanding and trust.

Contribution

It proposes a novel explanation layer for AI code assistants, reconstructing their thought process to make suggestions more transparent and understandable.

Findings

Design of CopilotLens as an explanation interface

Reconstruction of AI agent's thought process

Framework aims to improve trust and comprehension

Abstract

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.