In-Context Learning as an Effective Estimator of Functional Correctness of LLM-Generated Code
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915374462664704 |
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| author | Das, Susmita Ghosh, Madhusudan Swami, Priyanka Ganguly, Debasis Calikli, Gul |
| author_facet | Das, Susmita Ghosh, Madhusudan Swami, Priyanka Ganguly, Debasis Calikli, Gul |
| contents | When applying LLM-based code generation to software development projects that follow a feature-driven or rapid application development approach, it becomes necessary to estimate the functional correctness of the generated code in the absence of test cases. Just as a user selects a relevant document from a ranked list of retrieved ones, a software generation workflow requires a developer to choose (and potentially refine) a generated solution from a ranked list of alternative solutions, ordered by their posterior likelihoods. This implies that estimating the quality of a ranked list -- akin to estimating "relevance" for query performance prediction (QPP) in IR -- is also crucial for generative software development, where quality is defined in terms of "functional correctness". In this paper, we propose an in-context learning (ICL) based approach for code quality estimation. Our findings demonstrate that providing few-shot examples of functionally correct code from a training set enhances the performance of existing QPP approaches as well as a zero-shot-based approach for code quality estimation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_05200 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | In-Context Learning as an Effective Estimator of Functional Correctness of LLM-Generated Code Das, Susmita Ghosh, Madhusudan Swami, Priyanka Ganguly, Debasis Calikli, Gul Software Engineering Information Retrieval When applying LLM-based code generation to software development projects that follow a feature-driven or rapid application development approach, it becomes necessary to estimate the functional correctness of the generated code in the absence of test cases. Just as a user selects a relevant document from a ranked list of retrieved ones, a software generation workflow requires a developer to choose (and potentially refine) a generated solution from a ranked list of alternative solutions, ordered by their posterior likelihoods. This implies that estimating the quality of a ranked list -- akin to estimating "relevance" for query performance prediction (QPP) in IR -- is also crucial for generative software development, where quality is defined in terms of "functional correctness". In this paper, we propose an in-context learning (ICL) based approach for code quality estimation. Our findings demonstrate that providing few-shot examples of functionally correct code from a training set enhances the performance of existing QPP approaches as well as a zero-shot-based approach for code quality estimation. |
| title | In-Context Learning as an Effective Estimator of Functional Correctness of LLM-Generated Code |
| topic | Software Engineering Information Retrieval |
| url | https://arxiv.org/abs/2507.05200 |