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Exploring Large Language Models for Code Explanation

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arxiv 2310.16673 v1 pith:4T3QD26U submitted 2023-10-25 cs.SE cs.AIcs.IR

Paheli Bhattacharya , Manojit Chakraborty , Kartheek N S N Palepu , Vikas Pandey , Ishan Dindorkar , Rakesh Rajpurohit , Rishabh Gupta This is my paper · ORCID
classification cs.SEcs.AIcs.IR
keywords codelanguagellmslargemodelsautomatingbeneficialcounterparts
verification ladder T0 review T1 audit T2 compute T3 formal

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Automating code documentation through explanatory text can prove highly beneficial in code understanding. Large Language Models (LLMs) have made remarkable strides in Natural Language Processing, especially within software engineering tasks such as code generation and code summarization. This study specifically delves into the task of generating natural-language summaries for code snippets, using various LLMs. The findings indicate that Code LLMs outperform their generic counterparts, and zero-shot methods yield superior results when dealing with datasets with dissimilar distributions between training and testing sets.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Selective Shot Learning for Code Explanation

    cs.SE 2024-12 conditional novelty 6.0 of 10

    SSL_ner, a named-entity-based few-shot example selector, improves code explanation over token-based selection but not significantly over embedding-based selection across open-source Code-LLMs.

  2. Can the Rookies Cut the Tough Cookie? Exploring the Use of LLMs for SQL Equivalence Checking

    cs.DB 2024-12 conditional novelty 6.0 of 10

    LLMs, especially GPT-4, can classify SQL query equivalence on complex real-world assignment queries far beyond formal tools' coverage, but they systematically over-predict equivalence.

  3. ASSERTIFY: Utilizing Large Language Models to Generate Assertions for Production Code

    cs.SE 2024-11 conditional novelty 6.0 of 10

    A prompt-engineered LLM pipeline can generate production code assertions with up to 83.5% compile accuracy and 0.526 ROUGE-L similarity to developer-written assertions.

  4. From Critique to Clarity: A Pathway to Faithful and Personalized Code Explanations with Large Language Models

    cs.SE 2024-12 conditional novelty 4.0 of 10

    An iterative two-loop LLM pipeline (a faithfulness loop with execution-based checks and a personalization loop with a role-playing judge) produces code explanations that score higher on automatic metrics than simpler ...

  5. Evaluation of LLMs for mathematical problem solving

    cs.AI 2025-05 reject novelty 3.0 of 10

    A three-model, three-dataset LLM math evaluation using a multi-dimensional reasoning rubric, undermined by contradictory accuracy tables.

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