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Previous research on code intelligence usually trains a deep learning modelon a fixed dataset in an offline manner. However, in real-world scenarios, newcode repositories emerge incessantly, and the carried new knowledge isbeneficial for providing up-to-date code intelligence services to developers.In this paper, we aim at the following problem: How to enable code intelligencemodels to continually learn from ever-increasing data? One major challenge hereis catastrophic forgetting, meaning that the model can easily forget knowledgelearned from previous datasets when learning from the new dataset. To tacklethis challenge, we propose REPEAT, a novel method for continual learning ofcode intelligence models. Specifically, REPEAT addresses the catastrophicforgetting problem with representative exemplars replay and adaptive parameterregularization. The representative exemplars replay component selectsinformative and diverse exemplars in each dataset and uses them to retrainmodel periodically. The adaptive parameter regularization component recognizesimportant parameters in the model and adaptively penalizes their changes topreserve the knowledge learned before. We evaluate the proposed approach onthree code intelligence tasks including code summarization, softwarevulnerability detection, and code clone detection. Extensive experimentsdemonstrate that REPEAT consistently outperforms baseline methods on all tasks.For example, REPEAT improves the conventional fine-tuning method by 1.22, 5.61,and 1.72 on code summarization, vulnerability detection and clone detection,respectively.

Keeping Pace with Ever-Increasing Data: Towards Continual Learning of Code Intelligence Models