A Metamorphic Testing Approach to Diagnosing Memorization in LLM-Based Program Repair
LLM-based automated program repair (APR) techniques have shown promising results in reducing debugging costs. However, prior results can be affected by data leakage: large language models (LLMs) may memorize bug fixes when evaluation benchmarks overlap with their pretraining data, leading to inflated performance estimates. In this paper, we investigate whether we can better reveal data leakage by combining metamorphic testing (MT) with negative log-likelihood (NLL), which has been used in prior work as a proxy for memorization. We construct variant benchmarks by applying semantics-preserving transformations to two widely used datasets, Defects4J and GitBug-Java. Using these benchmarks, we...
| Authors | Milan De Koning, Ali Asgari, Pouria Derakhshanfar et al. |
|---|---|
| Primary category | cs.SE |
| Published | 23 April 2026 |
| Last updated at source | 23 April 2026 |
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