Towards Verified Code Reasoning by LLMs
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866911264065716224 |
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| author | Sistla, Meghana Balakrishnan, Gogul Rondon, Pat Cambronero, José Tufano, Michele Chandra, Satish |
| author_facet | Sistla, Meghana Balakrishnan, Gogul Rondon, Pat Cambronero, José Tufano, Michele Chandra, Satish |
| contents | While LLM-based agents are able to tackle a wide variety of code reasoning questions, the answers are not always correct. This prevents the agent from being useful in situations where high precision is desired: (1) helping a software engineer understand a new code base, (2) helping a software engineer during code review sessions, and (3) ensuring that the code generated by an automated code generation system meets certain requirements (e.g. fixes a bug, improves readability, implements a feature).
As a result of this lack of trustworthiness, the agent's answers need to be manually verified before they can be trusted. Manually confirming responses from a code reasoning agent requires human effort and can result in slower developer productivity, which weakens the assistance benefits of the agent. In this paper, we describe a method to automatically validate the answers provided by a code reasoning agent by verifying its reasoning steps. At a very high level, the method consists of extracting a formal representation of the agent's response and, subsequently, using formal verification and program analysis tools to verify the agent's reasoning steps.
We applied this approach to a benchmark set of 20 uninitialized variable errors detected by sanitizers and 20 program equivalence queries. For the uninitialized variable errors, the formal verification step was able to validate the agent's reasoning on 13/20 examples, and for the program equivalence queries, the formal verification step successfully caught 6/8 incorrect judgments made by the agent. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_26546 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Verified Code Reasoning by LLMs Sistla, Meghana Balakrishnan, Gogul Rondon, Pat Cambronero, José Tufano, Michele Chandra, Satish Software Engineering Machine Learning While LLM-based agents are able to tackle a wide variety of code reasoning questions, the answers are not always correct. This prevents the agent from being useful in situations where high precision is desired: (1) helping a software engineer understand a new code base, (2) helping a software engineer during code review sessions, and (3) ensuring that the code generated by an automated code generation system meets certain requirements (e.g. fixes a bug, improves readability, implements a feature). As a result of this lack of trustworthiness, the agent's answers need to be manually verified before they can be trusted. Manually confirming responses from a code reasoning agent requires human effort and can result in slower developer productivity, which weakens the assistance benefits of the agent. In this paper, we describe a method to automatically validate the answers provided by a code reasoning agent by verifying its reasoning steps. At a very high level, the method consists of extracting a formal representation of the agent's response and, subsequently, using formal verification and program analysis tools to verify the agent's reasoning steps. We applied this approach to a benchmark set of 20 uninitialized variable errors detected by sanitizers and 20 program equivalence queries. For the uninitialized variable errors, the formal verification step was able to validate the agent's reasoning on 13/20 examples, and for the program equivalence queries, the formal verification step successfully caught 6/8 incorrect judgments made by the agent. |
| title | Towards Verified Code Reasoning by LLMs |
| topic | Software Engineering Machine Learning |
| url | https://arxiv.org/abs/2509.26546 |