An LLM-Based Agent-Oriented Approach for Automated Code Design Issue Localization

Maintaining software design quality is crucial for the long-term maintainability and evolution of systems. However, design issues such as poor modularity and excessive complexity often emerge as codebases grow. Developers rely on external tools, such as program analysis techniques, to identify such...

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Bibliographic Details
Published in:Proceedings / International Conference on Software Engineering pp. 1320 - 1332
Main Authors: Batole, Fraol, OBrien, David, Nguyen, Tien N., Dyer, Robert, Rajan, Hridesh
Format: Conference Proceeding
Language:English
Published: IEEE 26.04.2025
Subjects:
Accuracy
Code Design Issue Localization
Codes
Complexity theory
Large language models
Large Language Models (LLMs)
Location awareness
Multi-Agent
Natural languages
Software design
Software engineering
Static Program Analysis
Technological innovation
Transforms
ISSN:1558-1225
Online Access:Get full text
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Summary:Maintaining software design quality is crucial for the long-term maintainability and evolution of systems. However, design issues such as poor modularity and excessive complexity often emerge as codebases grow. Developers rely on external tools, such as program analysis techniques, to identify such issues. This work leverages Large Language Models (LLMs) to develop an automated approach for analyzing and localizing design issues. Large language models have demonstrated significant performance on coding tasks, but directly leveraging them for design issue localization is challenging. Large codebases exceed typical LLM context windows, and program analysis tool outputs in non-textual modalities (e.g., graphs or interactive visualizations) are incompatible with LLMs' natural language inputs. To address these challenges, we propose LOCALIZEAGENT, a novel multi-agent framework for effective design issue localization. LOCALIZEAGENT integrates the specialized agents that (1) analyze code to identify potential code design issues, (2) transform program analysis outputs into abstraction-aware LLM-friendly natural language summaries, (3) generate context-aware prompts tailored to specific refactoring types, and (4) leverage LLMs to locate and rank the localized issues based on their relevance. Our evaluation using diverse real-world codebases demonstrates significant improvements over the baseline approaches, with LOCALIZEAGENT achieving 138 \%, 166 \% , and 206 % relative improvements in exact-match accuracy for localizing information hiding, complexity, and modularity issues, respectively.
ISSN:1558-1225
DOI:10.1109/ICSE55347.2025.00100