The Ideal LLM Workflow for Reducing Logic Illusions in Legacy Refactoring

Introduction: The Problem of Logical Hallucinations in Refactoring

Refactoring legacy complex architectures is one of the most critical tasks in the software lifecycle, but often LLMs (Large Language Models) introducelogical hallucinationsthat compromise code quality. This article explores a multi-model orchestration workflow to minimize these errors, ensuring structural correctness and architectural coherence.

Comparative Analysis of Reasoning Capabilities

GPT-4o: Versatility and Contextual Understanding

GPT-4o excels in contextual understanding and generating readable code but may introduce logical hallucinations in complex scenarios. Example:

def calcola_prezzo(quantita, prezzo_unitario):
# Hallucination: ignores discounts or taxes
return quantita * prezzo_unitario

Claude 3.5 Sonnet: Precision and Quality Control

Claude 3.5 Sonnet offers a better balance between creativity and precision, ideal for the 'critic' role in a workflow. Example of verification:

# Claude flags: missing input validation
if quantita     raise ValueError("Invalid parameters")

DeepSeek-Coder: Code Specialization

DeepSeek-Coder stands out for its ability to generate structured code but requires careful context management. Example:

def calcola_prezzo(quantita, prezzo_unitario, sconto=0):
totale = quantita * prezzo_unitario
return totale - (totale * sconto / 100)

Multi-Model Verification Strategy

Workflow Stages

• Initial Generation: Use DeepSeek-Coder for refactoring draft
• Critical Verification: Claude 3.5 Sonnet analyzes logic and potential errors
• Contextual Optimization: GPT-4o integrates the solution into the architectural context

Practical Example

Suppose we want to rewrite a legacy function:

# Original code
def processa_dati(dati):
risultato = []
for item in dati:
if item['tipo'] == 'A':
risultato.append(item['valore'] * 2)
else:
risultato.append(item['valore'])
return risultato

The multi-model workflow would produce:

# Optimized output
def processa_dati(dati):
if not isinstance(dati, list):
raise TypeError("Invalid input")
risultato = []
for item in dati:
valore = item.get('valore', 0)
if item.get('tipo') == 'A':
risultato.append(valore * 2)
else:
risultato.append(valore)
return risultato

Legacy Codebase Context Management

Intelligent Chunking

Split legacy modules into manageable logical units:

# Chunking example
original_module = {"functions": [...], "classes": [...]}
chunks = split_module(original_module, max_tokens=8000)

Synthesis Tools

• Use GPT-4o to generate dependency diagrams
• Claude 3.5 Sonnet to extract critical interfaces
• DeepSeek-Coder to rebuild isolated components

Metrics to Evaluate Logical Hallucinations

Key Indicators

ELE Calculation Example

ele = (critical_bugs_found / refactored_lines_of_code) * 1000

Conclusions and Recommendations

A multi-model workflow combining DeepSeek-Coder's specialization, Claude 3.5 Sonnet's precision, and GPT-4o's versatility significantly reduces logical hallucinations. The key is careful context management and quantitative metrics to assess correctness.

FAQ

What is the best LLM model for legacy code refactoring?

There is no universal model: DeepSeek-Coder for initial generation, Claude 3.5 Sonnet for critical verification, and GPT-4o for contextual integration.

How to handle very large legacy codebases?

Use chunking techniques to divide modules into logical units and synthesis tools to maintain architectural coherence during processing.