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

• URL: http://arxiv.org/abs/2601.10773v1
• Date: Thu, 15 Jan 2026 15:35:23 GMT
• Title: LogicLens: Leveraging Semantic Code Graph to explore Multi Repository large systems
• Authors: Niko Usai, Dario Montagnini, Kristian Ilianov Iliev, Raffaele Camanzo,
• Abstract summary: We introduce LogicLens, a reactive conversational agent that assists developers in exploring complex software systems.<n>We present the architecture of the system, discuss emergent behaviors, and evaluate its effectiveness on real-world multi-repository scenarios.
• Score: 0.2519906683279152
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Multi-CoLoR: Context-Aware Localization and Reasoning across Multi-Language Codebases [1.4216413758677147]
  We present Multi-CoLoR, a framework for Context-aware localization and reasoning across Multi-Languages.<n>It integrates organizational knowledge retrieval with graph-based reasoning to traverse complex software ecosystems.
  arXiv  Detail & Related papers  (2026-02-23T00:54:59Z)
• El Agente Gráfico: Structured Execution Graphs for Scientific Agents [7.47895130442454]
  We present El Agente Grfico, a single-agent framework that embeds large language models (LLMs)-driven decision-making within a type-safe execution environment.<n>Central to our approach is a structured abstraction of scientific concepts and an object-graph mapper that represents computational state as typed Python objects.<n>We evaluate the system by developing an automated benchmarking framework across a suite of university-level quantum chemistry tasks.
  arXiv  Detail & Related papers  (2026-02-19T23:47:05Z)
• CodeGlance: Understanding Code Reasoning Challenges in LLMs through Multi-Dimensional Feature Analysis [14.328535883908176]
  We present CodeGlance, a benchmark investigating code reasoning challenges across three realistic scenarios.<n>We find that unseen function reasoning poses significant challenges especially for smaller models.<n>We identify critical code complexity features that significantly impact code reasoning difficulty across scenarios.
  arXiv  Detail & Related papers  (2026-02-15T02:46:51Z)
• Multi-Agent Procedural Graph Extraction with Structural and Logical Refinement [66.51979814832332]
  model formulates procedural graph extraction as a multi-round reasoning process with dedicated structural and logical refinement.<n>Experiments demonstrate that model achieves substantial improvements in both structural correctness and logical consistency over strong baselines.
  arXiv  Detail & Related papers  (2026-01-27T04:00:48Z)
• Completion by Comprehension: Guiding Code Generation with Multi-Granularity Understanding [37.78627994991325]
  CoCo is a novel framework that enables code Completion by of multi-granularity context from large-scale code repositories.<n>Experiments on CrossCodeEval and RepoEval benchmarks demonstrate that CoCo consistently surpasses state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-12-04T07:37:59Z)
• Scalable and Explainable Enterprise Knowledge Discovery Using Graph-Centric Hybrid Retrieval [0.0]
  Modern enterprises manage vast knowledge distributed across heterogeneous systems such as Jira, Git repositories, Confluence, and wikis.<n>We present a modular hybrid retrieval framework that integrates Knowledge Base Language-Augmented Models (KBLam), DeepGraph representations, and embedding-driven semantic search.<n>The framework builds a unified knowledge graph from parsed repositories including code, pull requests, and commit histories.<n> Experiments on large-scale Git repositories show that the unified reasoning layer improves answer relevance by up to 80 percent compared with standalone GPT-based retrieval pipelines.
  arXiv  Detail & Related papers  (2025-10-13T02:56:36Z)
• Flash-Searcher: Fast and Effective Web Agents via DAG-Based Parallel Execution [48.7788770680643]
  Flash-Searcher is a novel parallel agent reasoning framework.<n>It decomposes complex tasks into subtasks with explicit dependencies, enabling concurrent execution of independent reasoning paths.<n>It achieves 67.7% accuracy on BrowseComp and 83% on xbench-DeepSearch, while reducing agent execution steps by up to 35% compared to current frameworks.
  arXiv  Detail & Related papers  (2025-09-29T17:39:30Z)
• Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning [62.640169289390535]
  SPLIT-RAG is a multi-agent RAG framework that addresses the limitations with question-driven semantic graph partitioning and collaborative subgraph retrieval.<n>The innovative framework first create Semantic Partitioning of Linked Information, then use the Type-Specialized knowledge base to achieve Multi-Agent RAG.<n>The attribute-aware graph segmentation manages to divide knowledge graphs into semantically coherent subgraphs, ensuring subgraphs align with different query types.<n>A hierarchical merging module resolves inconsistencies across subgraph-derived answers through logical verifications.
  arXiv  Detail & Related papers  (2025-05-20T06:44:34Z)
• EpiCoder: Encompassing Diversity and Complexity in Code Generation [66.43738008739555]
  Existing methods for code generation use code snippets as seed data.<n>We introduce a novel feature tree-based synthesis framework, which revolves around hierarchical code features.<n>Our framework provides precise control over the complexity of the generated code, enabling functionalities that range from function-level operations to multi-file scenarios.
  arXiv  Detail & Related papers  (2025-01-08T18:58:15Z)
• Improving Complex Reasoning over Knowledge Graph with Logic-Aware Curriculum Tuning [89.89857766491475]
  We propose a curriculum-based logical-aware instruction tuning framework, named LACT.<n>Specifically, we augment the arbitrary first-order logical queries via binary tree decomposition.<n> Experiments across widely used datasets demonstrate that LACT has substantial improvements(brings an average +5.5% MRR score) over advanced methods, achieving the new state-of-the-art.
  arXiv  Detail & Related papers  (2024-05-02T18:12:08Z)
• Conversational Semantic Parsing using Dynamic Context Graphs [68.72121830563906]
  We consider the task of conversational semantic parsing over general purpose knowledge graphs (KGs) with millions of entities, and thousands of relation-types. We focus on models which are capable of interactively mapping user utterances into executable logical forms.
  arXiv  Detail & Related papers  (2023-05-04T16:04:41Z)
• A Unified Active Learning Framework for Annotating Graph Data with Application to Software Source Code Performance Prediction [4.572330678291241]
  We develop a unified active learning framework specializing in software performance prediction. We investigate the impact of using different levels of information for active and passive learning. Our approach aims to improve the investment in AI models for different software performance predictions.
  arXiv  Detail & Related papers  (2023-04-06T14:00:48Z)
• LogicalFactChecker: Leveraging Logical Operations for Fact Checking with Graph Module Network [111.24773949467567]
  We propose LogicalFactChecker, a neural network approach capable of leveraging logical operations for fact checking. It achieves the state-of-the-art performance on TABFACT, a large-scale, benchmark dataset.
  arXiv  Detail & Related papers  (2020-04-28T17:04:19Z)

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.