TransCoder: Towards Unified Transferable Code Representation Learning Inspired by Human Skills
This work addresses the problem of efficient adaptation of code models for software intelligence tasks, offering a more cost-effective solution, though it is incremental as it builds on existing pre-trained models.
The paper tackles the high cost and data requirements of fine-tuning code pre-trained models on individual tasks by introducing TransCoder, a unified transferable fine-tuning strategy that improves performance on various code-related tasks, with experiments showing benefits in low-resource scenarios.
Code pre-trained models (CodePTMs) have recently demonstrated a solid capacity to process various software intelligence tasks, e.g., code clone detection, code translation, and code summarization. The current mainstream method that deploys these models to downstream tasks is to fine-tune them on individual tasks, which is generally costly and needs sufficient data for large models. To tackle the issue, in this paper, we present TransCoder, a unified Transferable fine-tuning strategy for Code representation learning. Inspired by human inherent skills of knowledge generalization, TransCoder drives the model to learn better code-related meta-knowledge like human programmers. Specifically, we employ a tunable prefix encoder as the meta-learner to capture cross-task and cross-language transferable knowledge, respectively. Besides, tasks with minor training sample sizes and languages with small corpus can be remarkably benefited from our approach. Extensive experiments conducted on benchmark datasets clearly demonstrate that our method can lead to superior performance on various code-related tasks and encourage mutual reinforcement. We also show that TransCoder is applicable in low-resource scenarios. Our codes are available at https://github.com/QiushiSun/TransCoder.