Choose Your Own Adventure: Non-Linear AI-Assisted Programming with EvoGraph

2026-04-20

AI Summary

Current AI-powered programming assistants predominantly rely on linear chat interfaces, which hinder developers’ ability to non-linearly explore multiple solution paths and trace code modifications. To address this limitation, this work proposes EvoGraph—the first IDE plugin that models AI-assisted programming as an interactive development graph. EvoGraph explicitly captures the history of prompts, AI responses, and code changes in a graph structure, enabling branch exploration, state comparison, and rollback. By integrating automated history tracking, graph-based visualization, and state management, the approach provides a structured representation of human–AI collaboration. User studies demonstrate that EvoGraph significantly reduces cognitive load, enhances the safety and efficiency of iterative exploration, and strengthens developers’ understanding of and control over AI-generated code.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Intelligent Robots: Embodied AI

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Current AI-assisted programming tools are predominantly linear and chat-based, which deviates from the iterative and branching nature of programming itself. Our preliminary study with developers using AI assistants suggested that they often struggle to explore alternatives, manage prompting sequences, and trace changes. Informed by these insights, we created EvoGraph, an IDE plugin that integrates AI interactions and code changes as a lightweight and interactive development graph. EvoGraph automatically records a branching AI-assisted coding history and allows developers to manipulate the graph to compare, merge, and revisit prior collaborative AI programming states. Our user study with 20 participants revealed that EvoGraph addressed developers' challenges identified in our preliminary study while imposing lower cognitive load. Participants also found the graph-based representation supported safe exploration, efficient iteration, and reflection on AI-generated changes. Our work highlights design opportunities for tools to help developers make sense of and act on their problem-solving progress in the emerging AI-mediated programming context.

Problem

• AI-assisted programming
• non-linear programming
• development graph
• code exploration
• programming iteration

Innovation

• non-linear programming
• AI-assisted development
• development graph
• branching history
• interactive IDE plugin