Beyond Autocomplete: Designing CopilotLens Towards Transparent and Explainable AI Coding Agents
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915505609113600 |
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| author | Ye, Runlong Zhang, Zeling Almazroua, Boushra Liut, Michael |
| author_facet | Ye, Runlong Zhang, Zeling Almazroua, Boushra Liut, Michael |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_20062 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond Autocomplete: Designing CopilotLens Towards Transparent and Explainable AI Coding Agents Ye, Runlong Zhang, Zeling Almazroua, Boushra Liut, Michael Human-Computer Interaction Artificial Intelligence 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. |
| title | Beyond Autocomplete: Designing CopilotLens Towards Transparent and Explainable AI Coding Agents |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2506.20062 |