LLM Assisted Coding with Metamorphic Specification Mutation Agent
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911282747146240 |
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| author | Akhond, Mostafijur Rahman Uddin, Gias |
| author_facet | Akhond, Mostafijur Rahman Uddin, Gias |
| contents | Metamorphic Relations (MRs) serve as a foundational mechanism for generating semantically equivalent mutations. Software engineering has advanced significantly in recent years with the advent of Large Language Models (LLMs). However, the reliability of LLMs in software engineering is often compromised by ambiguities and inconsistencies due to improper user specification. To address this challenge, we present CodeMetaAgent (CMA), a metamorphic relation-driven LLM agent that systematically refines task specifications and generates semantically constrained test cases. Our proposed framework uses MRs with LLMs to improve generation consistency and reduce variability caused by specifications, unlike the traditional use of MRs as post validations. Our framework has been evaluated on the HumanEval-Pro, MBPP-Pro, and SWE-Bench_Lite datasets using the GPT-4o, Mistral Large, GPT-OSS, and Qwen3-Coder models. It improved code generation accuracy by up to 17% and achieved code coverage gains of up to 99.81%. These results show that metamorphic relations can be a simple but effective guide in assisting LLM-based software development. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_18249 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LLM Assisted Coding with Metamorphic Specification Mutation Agent Akhond, Mostafijur Rahman Uddin, Gias Software Engineering Metamorphic Relations (MRs) serve as a foundational mechanism for generating semantically equivalent mutations. Software engineering has advanced significantly in recent years with the advent of Large Language Models (LLMs). However, the reliability of LLMs in software engineering is often compromised by ambiguities and inconsistencies due to improper user specification. To address this challenge, we present CodeMetaAgent (CMA), a metamorphic relation-driven LLM agent that systematically refines task specifications and generates semantically constrained test cases. Our proposed framework uses MRs with LLMs to improve generation consistency and reduce variability caused by specifications, unlike the traditional use of MRs as post validations. Our framework has been evaluated on the HumanEval-Pro, MBPP-Pro, and SWE-Bench_Lite datasets using the GPT-4o, Mistral Large, GPT-OSS, and Qwen3-Coder models. It improved code generation accuracy by up to 17% and achieved code coverage gains of up to 99.81%. These results show that metamorphic relations can be a simple but effective guide in assisting LLM-based software development. |
| title | LLM Assisted Coding with Metamorphic Specification Mutation Agent |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2511.18249 |