On-the-Fly Input Adaptation for Reliable Code Intelligence

2026-05-19

AI Summary

This work addresses the susceptibility of existing code language models to misjudgments in practical applications, a limitation exacerbated by mainstream repair methods that are costly, poorly generalizable, and unstable. To overcome these challenges, the paper proposes a two-stage input dynamic adaptation framework that requires neither model parameter updates nor additional supervision. Operating at inference time without retraining or architectural modifications, the approach dynamically refines model inputs through runtime validation and syntax-semantic-preserving code transformations. Empirical results demonstrate that this method substantially reduces misjudgment rates across diverse code understanding tasks, significantly enhancing model accuracy while maintaining high scalability and computational efficiency. The framework thus establishes a novel paradigm for improving the reliability of code intelligence models.

Abstract

Code language models (CLMs) play a central role in software engineering across both generation and classification tasks. However, these models still exhibit notable mispredictions in real-world applications, even when trained on up-to-date data. Existing solutions address this by retraining the model, modifying its architecture, or re-engineering prompts. These approaches incur high computational cost requiring substantial effort in data labeling, model updates, and redeployment, and often suffer from poor generalization across tasks and tuning instability across models. This work proposes an alternative strategy based on on-the-fly input adaptation, which improves model behavior without altering its parameters or requiring additional supervision. The method consists of two stages: input validation, which detects inputs likely to cause mispredictions, and input adaptation, which transforms them using syntax- and semantics-preserving operations to better align with the model's learned behavior. This dual strategy reduces mispredictions across diverse code understanding tasks, boosting model performance without necessitating retraining. As a scalable and resource-efficient solution, this framework holds significant promise for high-stakes applications in software engineering where reliability is critical.

Problem

• code language models
• mispredictions
• input adaptation
• reliability
• software engineering

Innovation

• on-the-fly input adaptation
• code language models
• input validation
• syntax-preserving transformation
• reliable code intelligence