Information-Theoretic Corrective Example Selection (IT-CES): Calibrated In-Context Example Selection for Automated Code Correction

In corrective in-context learning for automated code repair, the effectiveness of Large Language Model (LLM) self-correction depends substantially on the quality of retrieved demonstrations. Standard similarity-based selection methods, such as Top-K retrieval, often select examples independently and ignore correlations among candidates, leading to redundant contexts with diminishing marginal utility. We propose Information-Theoretic Corrective Example Selection (IT-CES), a theory-motivated framework for selecting a compact set of corrective demonstrations that jointly cover complementary repair patterns for the current failure. IT-CES represents each correction instance using a diagnostic tuple that combines the problem, the buggy solution, and the observed failure signal, and applies a calibrated log-determinant selection objective to choose examples that are both relevant to the current failure and complementary to one another. The calibration step mitigates the concentration of dense-retrieval similarity scores, improving the contrast of the diagnostic kernel used by the log-determinant objective. Across three code-repair benchmarks, IT-CES consistently improves Pass@1 repair accuracy over representative similarity-based and diversity-aware retrieval methods across the evaluated context budgets, generators, and dense retrievers. We further report step-by-step component ablations, paired exact statistical tests, intrinsic retrieval diagnostics, and multi-round dynamic repair evaluations. These results support the effectiveness of calibrated diagnostic example selection within the tested repair settings. The implementation is uploaded as supplementary material.