Quantum-Guided Test Case Minimization for LLM-Based Code Generation
Huixiang Zhang, Mahzabeen Emu
TL;DR
The paper tackles the problem of controlling LLM-generated code by integrating Test-Driven Development with Test Case Minimization and formulates the core optimization as a Quadratic Unconstrained Binary Optimization problem using binary variables $t_i$ and a penalty-based objective, enabling solution on both classical and quantum hardware ($\min$ expression provided). Quantum annealing delivers a major speedup over simulated annealing on the core TCM task, while the end-to-end framework reduces total token usage by $36.5\%$, lowers cyclomatic complexity by $26.1\%$, and accelerates generation by $54.3\%$, demonstrating practical gains for CI/CD pipelines. The work highlights a productive synthesis of generative AI and combinatorial optimization and suggests a path toward more efficient, controllable code generation with precise test specifications. It also discusses the critical role of careful model formulation in realizing the potential of quantum-accelerated software engineering.
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.