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

• URL: http://arxiv.org/abs/2506.20062v3
• Date: Sun, 21 Sep 2025 15:50:29 GMT
• Title: Beyond Autocomplete: Designing CopilotLens Towards Transparent and Explainable AI Coding Agents
• Authors: Runlong Ye, Zeling Zhang, Boushra Almazroua, Michael Liut,
• Abstract summary: CopilotLens is an interactive framework that reframes code completion from a simple suggestion into a transparent, explainable interaction.<n>CopilotLens operates as an explanation layer that reconstructs the AI agent's "thought process" through a dynamic, two-level interface.
• Score: 4.960232980231203
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• ClarEval: A Benchmark for Evaluating Clarification Skills of Code Agents under Ambiguous Instructions [19.875754116636436]
  We introduce ClarEval, a framework designed to assess an agent's "Collaborative Quotient" by simulating the inherent ambiguity of human communication.<n>To quantify this capability, we propose a metric suite led by Average Turns to Clarify coders (ATC) and Key Question Coverage (KQC)<n>Our experiments on eleven state-of-the-art agents reveal a stark reality: while models like GPT-5-Coder excel at coding, they often lack the strategic communication skills required for efficient partnership.
  arXiv  Detail & Related papers  (2026-02-27T01:10:27Z)
• Steering LLMs via Scalable Interactive Oversight [74.12746881843044]
  Large Language Models increasingly automate complex, long-horizon tasks such as emphvibe coding, a supervision gap has emerged.<n>It presents a critical challenge in scalable oversight: enabling humans to responsibly steer AI systems on tasks that surpass their own ability to specify or verify.
  arXiv  Detail & Related papers  (2026-02-04T04:52:00Z)
• Talk Less, Verify More: Improving LLM Assistants with Semantic Checks and Execution Feedback [14.593478824805542]
  This paper introduces two complementary verification techniques: Q*, which performs reverse translation and semantic matching between code and user intent, and Feedback+, which incorporates execution feedback to guide code refinement.<n> Evaluations on three benchmark datasets, Spider, Bird, and GSM8K, demonstrate that both Q* and Feedback+ reduce error rates and task completion time.
  arXiv  Detail & Related papers  (2026-01-01T06:10:06Z)
• Simple Agents Outperform Experts in Biomedical Imaging Workflow Optimization [69.36509281190662]
  Adapting production-level computer vision tools to bespoke scientific datasets is a critical "last mile" bottleneck.<n>We consider using AI agents to automate this manual coding, and focus on the open question of optimal agent design.<n>We demonstrate that a simple agent framework consistently generates adaptation code that outperforms human-expert solutions.
  arXiv  Detail & Related papers  (2025-12-02T18:42:26Z)
• Vibe Coding: Toward an AI-Native Paradigm for Semantic and Intent-Driven Programming [0.0]
  This paper introduces vibe coding, an emerging AI-native programming paradigm in which a developer specifies high-level functional intent along with qualitative descriptors of the desired "vibe"<n>An intelligent agent then transforms those specifications into executable software.
  arXiv  Detail & Related papers  (2025-10-09T22:31:53Z)
• AgentMesh: A Cooperative Multi-Agent Generative AI Framework for Software Development Automation [0.0]
  We propose a Python-based framework that uses multiple cooperating LLM-powered agents to automate software development tasks.<n>In AgentMesh, specialized agents - a Planner, Coder, Debugger, and Reviewer - work in concert to transform a high-level requirement into fully realized code.
  arXiv  Detail & Related papers  (2025-07-26T10:10:02Z)
• Code with Me or for Me? How Increasing AI Automation Transforms Developer Workflows [66.1850490474361]
  We conduct the first academic study to explore developer interactions with coding agents.<n>We evaluate two leading copilot and agentic coding assistants, GitHub Copilot and OpenHands.<n>Our results show agents have the potential to assist developers in ways that surpass copilots.
  arXiv  Detail & Related papers  (2025-07-10T20:12:54Z)
• Exploring Prompt Patterns in AI-Assisted Code Generation: Towards Faster and More Effective Developer-AI Collaboration [3.1861081539404137]
  This paper explores the application of structured prompt patterns to minimize the number of interactions required for satisfactory AI-assisted code generation.<n>We analyzed seven distinct prompt patterns to evaluate their effectiveness in reducing back-and-forth communication between developers and AI.
  arXiv  Detail & Related papers  (2025-06-02T12:43:08Z)
• Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI [0.36868085124383626]
  Review presents a comprehensive analysis of two emerging paradigms in AI-assisted software development: vibe coding and agentic coding.<n> Vibe coding emphasizes intuitive, human-in-the-loop interaction through prompt-based, conversational interaction.<n>Agentic coding enables autonomous software development through goal-driven agents capable of planning, executing, testing, and iterating tasks with minimal human intervention.
  arXiv  Detail & Related papers  (2025-05-26T03:00:21Z)
• Scratch Copilot: Supporting Youth Creative Coding with AI [7.494510764739512]
  We present Cognimates Scratch Copilot: an AI-powered assistant integrated into a Scratch-like environment.<n>This paper details the system architecture and findings from an exploratory qualitative evaluation with 18 international children.
  arXiv  Detail & Related papers  (2025-05-06T17:13:29Z)
• Interacting with AI Reasoning Models: Harnessing "Thoughts" for AI-Driven Software Engineering [11.149764135999437]
  Recent advances in AI reasoning models provide unprecedented transparency into their decision-making processes.<n>Software engineers rarely have the time or cognitive bandwidth to analyze, verify, and interpret every AI-generated thought in detail.<n>We propose a vision for structuring the interaction between AI reasoning models and software engineers to maximize trust, efficiency, and decision-making power.
  arXiv  Detail & Related papers  (2025-03-01T13:19:15Z)
• Code to Think, Think to Code: A Survey on Code-Enhanced Reasoning and Reasoning-Driven Code Intelligence in LLMs [53.00384299879513]
  In large language models (LLMs), code and reasoning reinforce each other.<n>Code provides verifiable execution paths, enforces logical decomposition, and enables runtime validation.<n>We identify key challenges and propose future research directions to strengthen this synergy.
  arXiv  Detail & Related papers  (2025-02-26T18:55:42Z)
• Codev-Bench: How Do LLMs Understand Developer-Centric Code Completion? [60.84912551069379]
  We present the Code-Development Benchmark (Codev-Bench), a fine-grained, real-world, repository-level, and developer-centric evaluation framework. Codev-Agent is an agent-based system that automates repository crawling, constructs execution environments, extracts dynamic calling chains from existing unit tests, and generates new test samples to avoid data leakage.
  arXiv  Detail & Related papers  (2024-10-02T09:11:10Z)
• Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning [50.47568731994238]
  Key method for creating Artificial Intelligence (AI) agents is Reinforcement Learning (RL) This paper presents a general framework model for integrating and learning structured reasoning into AI agents' policies.
  arXiv  Detail & Related papers  (2023-12-22T17:57:57Z)
• Octopus: Embodied Vision-Language Programmer from Environmental Feedback [58.04529328728999]
  Embodied vision-language models (VLMs) have achieved substantial progress in multimodal perception and reasoning. To bridge this gap, we introduce Octopus, an embodied vision-language programmer that uses executable code generation as a medium to connect planning and manipulation. Octopus is designed to 1) proficiently comprehend an agent's visual and textual task objectives, 2) formulate intricate action sequences, and 3) generate executable code.
  arXiv  Detail & Related papers  (2023-10-12T17:59:58Z)
• From Copilot to Pilot: Towards AI Supported Software Development [3.0585424861188844]
  We study the limitations of AI-supported code completion tools like Copilot and offer a taxonomy for understanding the classification of AI-supported code completion tools in this space. We then conduct additional investigation to determine the current boundaries of AI-supported code completion tools like Copilot. We conclude by providing a discussion on challenges for future development of AI-supported code completion tools to reach the design level of abstraction in our taxonomy.
  arXiv  Detail & Related papers  (2023-03-07T18:56:52Z)
• Generation Probabilities Are Not Enough: Uncertainty Highlighting in AI Code Completions [54.55334589363247]
  We study whether conveying information about uncertainty enables programmers to more quickly and accurately produce code. We find that highlighting tokens with the highest predicted likelihood of being edited leads to faster task completion and more targeted edits.
  arXiv  Detail & Related papers  (2023-02-14T18:43:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.