Beyond Memorization: Evaluating the True Type Inference Capabilities of LLMs for Java Code Snippets

• URL: http://arxiv.org/abs/2503.04076v1
• Date: Thu, 06 Mar 2025 04:13:40 GMT
• Title: Beyond Memorization: Evaluating the True Type Inference Capabilities of LLMs for Java Code Snippets
• Authors: Yiwen Dong, Zhenyang Xu, Yongqiang Tian, Chengnian Sun,
• Abstract summary: Recent studies have leveraged Large Language Models for type inference on code snippets, showing promising results.<n>However, these results are potentially affected by data leakage, as the benchmark suite (StatType-SO) has been public on GitHub since 2017.<n>We conducted a three-pronged evaluation to comprehensively assess LLMs' type inference capabilities on Java code snippets.
• Score: 3.152174935904172
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Type inference is a crucial task for reusing online code snippets, often found on platforms like StackOverflow, which frequently lack essential type information such as fully qualified names (FQNs) and required libraries. Recent studies have leveraged Large Language Models (LLMs) for type inference on code snippets, showing promising results. However, these results are potentially affected by data leakage, as the benchmark suite (StatType-SO) has been public on GitHub since 2017 (full suite in 2023). Thus, it is uncertain whether LLMs' strong performance reflects genuine code semantics understanding or a mere retrieval of ground truth from training data. To comprehensively assess LLMs' type inference capabilities on Java code snippets, we conducted a three-pronged evaluation. First, utilizing Thalia, a program synthesis technique, we created ThaliaType--a new, unseen dataset for type inference evaluation. On unseen snippets, LLM performance dropped significantly, with up to a 59% decrease in precision and 72% in recall. Second, we developed semantic-preserving transformations that significantly degraded LLMs' type inference performance, revealing weaknesses in understanding code semantics. Third, we used delta debugging to identify the minimal syntax elements sufficient for LLM inference. While type inference primarily involves inferring FQNs for types in the code snippet, LLMs correctly infer FQNs even when the types were absent from the snippets, suggesting a reliance on knowledge from training instead of thoroughly analyzing the snippets. Our findings indicate that LLMs' strong past performance likely stemmed from data leakage, rather than a genuine understanding of the semantics of code snippets. Our findings highlight the crucial need for carefully designed benchmarks using unseen code snippets to assess the true capabilities of LLMs for type inference tasks.

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