Quantum-Guided Test Case Minimization for LLM-Based Code Generation

2025-11-19

AI Summary

This work addresses the problem of redundant and inefficient code generation by large language models (LLMs). To tackle this, we propose an end-to-end code optimization framework driven by test-case minimization. Our core method formulates test-suite minimization as a Quadratic Unconstrained Binary Optimization (QUBO) problem—enabling seamless integration with both classical and quantum solvers—and synergistically combines LLM-generated test cases with hybrid quantum annealing (QA) and simulated annealing (SA) for combinatorial optimization. This represents the first integration of generative AI with QUBO-based optimization in code refinement. Experimental results demonstrate that QA achieves a 16× speedup over SA in solving the QUBO formulation. Overall, our framework reduces token consumption by 36.5%, significantly enhancing both the conciseness and functional correctness of generated code.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Quantum Machine Learning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

Precisely controlling Large Language Models (LLMs) to generate efficient and concise code is a central challenge in software engineering. We introduce a framework based on Test-Driven Development (TDD) that transforms code specification into a combinatorial optimization task. The framework first prompts an LLM to generate a test suite, then formulates the Test Case Minimization (TCM) problem as a Quadratic Unconstrained Binary Optimization (QUBO) model. This QUBO paradigm is compatible with both classical solvers and emerging hardware such as quantum annealers. Experimentally, quantum annealing solves the core TCM task 16 times faster than simulated annealing. This performance underpins our end-to-end framework, which reduces total token consumption by 36.5% and significantly improves code quality. This work demonstrates a powerful synergy between generative AI and combinatorial optimization in software engineering, highlighting the critical importance of precise model formulation.

Problem

• Minimizing test cases for LLM-generated code using quantum optimization
• Reducing token consumption while improving generated code quality
• Formulating test case minimization as quadratic unconstrained binary optimization

Innovation

• Uses quantum annealing for test case minimization
• Formulates minimization as quadratic binary optimization
• Reduces token consumption while improving code quality