TLDR: This research introduces an AI-guided system that combines large language models (LLMs) with traditional reverse engineering to help developers understand large and complex codebases more effectively. It offers an interactive, adaptive, and collaborative visual exploration experience, aiming to reduce cognitive load and improve program comprehension by integrating structural, semantic, and social information.
Understanding large and complex software systems is a significant challenge for developers, who often spend a majority of their time just trying to comprehend unfamiliar code. This task becomes even harder with modern systems that feature multiple layers, distributed components, and often incomplete documentation.
Traditional tools designed to help with program comprehension, such as those for static analysis or software visualization, often fall short. They tend to offer static views, lack interactivity, struggle to scale with project size, and force developers to switch between different tools, increasing their mental effort.
Recent advancements in large language models (LLMs) present exciting new possibilities for assisting developers. LLMs can generate summaries, suggest exploration paths, and answer questions about code. However, their practical use in this area has been limited by concerns about accuracy, a lack of direct connection to the code’s structure, and poor integration with interactive visual tools.
A new research paper, titled “AI-Guided Exploration of Large-Scale Codebases,” by Yoseph Berhanu Alebachew from Virginia Tech, addresses these challenges. This work explores how LLMs can be combined with precise reverse engineering techniques to create adaptive, multi-level, and context-aware tools for code understanding. The focus is on enabling a more fluid and guided interaction between the developer and the software system, blending structural representations, semantic insights, and contextual information to effectively explore vast codebases. You can read the full paper here: AI-Guided Exploration of Large-Scale Codebases.
Bridging the Gap in Code Comprehension Tools
The research builds upon existing work in software visualization and program comprehension. While tools like SHriMP and CodeCity provide static visual models of software, they often don’t support dynamic understanding or integrate historical and contextual information. Interactive interfaces such as CodeBubbles improved engagement but lacked integration with reverse engineering or natural language guidance. Collaborative tools like LiveShare support real-time team navigation but don’t offer architectural visualization or deep semantic understanding.
The proposed approach advances the field by integrating an LLM agent directly into the visualization process. This moves beyond simple static diagrams to support adaptive, interactive, and intent-aware navigation. The LLM acts as a reasoning layer, responding to user interactions like clicks and filters, and suggesting exploration paths or summarizing changes. This system is designed to be one of the first to unify static code structure, dynamic visualization, semantic context extraction, and collaborative user interface guidance within a single framework.
How the AI-Supported System Works
The system combines deterministic reverse engineering with LLM-guided, intent-aware interaction through four core components:
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Code-to-UML Reverse Engineering: This component parses source code to generate UML diagrams, allowing for multi-level abstraction and modular breakdown, supporting both top-down and bottom-up understanding.
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Interactive Visualization: The front-end provides dynamic visualizations with features like zooming, panning, and drill-down. It can also include overlays like change frequency heatmaps and historical comparisons.
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LLM-Guided Interface Planner: The LLM interprets user queries and interactions, recommending guided exploration paths, providing contextual summaries, and dynamically updating the interface. It can learn from past exploration traces, both individual and team-based, to improve its guidance.
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Context and Collaboration Layer: This enriches visualizations with information from version control and supports collaborative features like shared views, real-time annotations, and embedded documentation, helping distributed teams maintain a shared understanding.
These components create a continuous interaction loop: code structure is visualized, the user explores or queries, the LLM refines the view based on intent and context, and the updated visualization guides the next step. This transforms code exploration into an adaptive, multi-modal process that combines structural, semantic, and social signals.
Looking Ahead
A functional prototype has been developed, currently supporting Java programs, demonstrating the feasibility of this approach. Future work includes conducting user studies to evaluate the system’s impact on comprehension accuracy, task completion time, and perceived cognitive load. The aim is also to extend the system to handle larger and more complex codebases, integrate runtime behavior analysis, and support real-time collaborative exploration.
The researchers also plan to investigate using Graphical User Interface (GUI)–based interaction as a primary way to integrate LLMs, moving beyond traditional chat interfaces. Key challenges remain, such as ensuring the accuracy and trustworthiness of LLM-generated guidance, managing long-term interaction context, and scaling the system to massive codebases that use multiple programming languages.
Also Read:
- Enhancing Code Completion with Adaptive Context Filtering
- Enhancing AI Agent Tool Selection with Knowledge Graphs for Enterprise Tasks
Conclusion
This AI-guided approach to large-scale program comprehension offers a promising way to bridge traditional reverse engineering with modern LLM-based interaction. By combining structural visualization with conversational guidance, the goal is to reduce developer cognitive load and enable a more intuitive and strategic exploration of software systems. This hybrid design lays the groundwork for a new generation of software understanding tools that are explainable, user-driven, and closely aligned with how developers think and work.
