Quantum-Guided Test Case Minimization for LLM-Based Code Generation
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914164531789824 |
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| author | Zhang, Huixiang Emu, Mahzabeen |
| author_facet | Zhang, Huixiang Emu, Mahzabeen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_15665 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum-Guided Test Case Minimization for LLM-Based Code Generation Zhang, Huixiang Emu, Mahzabeen Software Engineering 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. |
| title | Quantum-Guided Test Case Minimization for LLM-Based Code Generation |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2511.15665 |