While leveraging Large Language Models (LLMs) like ChatGPT for program repair offers numerous benefits, there are several limitations and drawbacks associated with heavy reliance on these models:
1.Limited Generalization: LLMs may struggle with generalizing fixes across diverse codebases due to overfitting tendencies towards specific patterns seen during training.
2Data Bias: The performance of LLMs is highly dependent on the quality and representativeness of the training data; biased datasets may lead to biased outputs affecting repair accuracy.
3Resource Intensive: Continuous querying APIs like ChatGPT incurs high costs making it impractical for real-time usage without careful budget considerations.
4Interpretability Challenges: Understanding why an LLM generates a particular fix may be challenging due to their complex internal workings hindering transparency.
5Ethical Concerns: Unintended biases encoded within large-scale pre-trained models may propagate into repaired code leading to ethical implications if not carefully monitored.
These limitations underscore the importance of complementing LLM-based approaches with human oversight, domain knowledge integration, and robust evaluation frameworks when applying them extensively in program repair contexts