LogicLens: Leveraging Semantic Code Graph to explore Multi Repository large systems

TL;DR

LogicLens is a system that uses a semantic code graph enriched with language models to help developers explore and understand large, multi-repository software systems through natural language interaction.

Contribution

It introduces a novel semantic multi-repository graph combining syntactic analysis and LLM-based semantic enrichment for interactive system exploration.

Findings

Effective in real-world multi-repository scenarios

Enables impact analysis and debugging through semantic graph

Supports natural language queries for complex system understanding

Abstract

Understanding large software systems is a challenging task, especially when code is distributed across multiple repositories and microservices. Developers often need to reason not only about the structure of the code, but also about its domain logic and runtime behaviors, which are typically implicit and scattered. We introduce LogicLens, a reactive conversational agent that assists developers in exploring complex software systems through a semantic multi-repository graph. This graph is built in a preprocessing step by combining syntactic code analysis, via AST parsing and repository traversal, with semantic enrichment using Large Language Models (LLMs). The resulting graph captures both structural elements, such as files, classes, and functions, as well as functional abstractions like domain entities, operations, and workflows. Once the graph is constructed, LogicLens enables developers to interact with it via natural language, dynamically retrieving relevant subgraphs and answering technical or functional queries. We present the architecture of the system, discuss emergent behaviors, and evaluate its effectiveness on real-world multi-repository scenarios. We demonstrate emergent capabilities including impact analysis and symptom-based debugging that arise naturally from the semantic graph structure.