Utilization of Pre-trained Language Model for Adapter-based Knowledge Transfer in Software Engineering

• URL: http://arxiv.org/abs/2307.08540v2
• Date: Tue, 6 Feb 2024 08:03:31 GMT
• Title: Utilization of Pre-trained Language Model for Adapter-based Knowledge Transfer in Software Engineering
• Authors: Iman Saberi, Fatemeh Fard and Fuxiang Chen
• Abstract summary: We study the knowledge transfer using adapters on multiple down-stream tasks including cloze test, code clone detection, and code summarization. adapters are trained on code corpora and are inserted into a PLM that is pre-trained on English corpora or code corpora. We observed an improvement in results using NL-PLM over a PLM that does not have adapters, and this suggested that adapters can transfer and utilize useful knowledge from NL-PLM to SE tasks.
• Score: 0.3963827913892984
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Software Engineering (SE) Pre-trained Language Models (PLMs), such as CodeBERT, are pre-trained on large code corpora, and their learned knowledge has shown success in transferring into downstream tasks (e.g., code clone detection) through the fine-tuning of PLMs. In Natural Language Processing (NLP), an alternative in transferring the knowledge of PLMs is explored through the use of adapter, a compact and parameter efficient module that is inserted into a PLM. Although the use of adapters has shown promising results in many NLP-based downstream tasks, their application and exploration in SE-based downstream tasks are limited. Here, we study the knowledge transfer using adapters on multiple down-stream tasks including cloze test, code clone detection, and code summarization. These adapters are trained on code corpora and are inserted into a PLM that is pre-trained on English corpora or code corpora. We called these PLMs as NL-PLM and C-PLM, respectively. We observed an improvement in results using NL-PLM over a PLM that does not have adapters, and this suggested that adapters can transfer and utilize useful knowledge from NL-PLM to SE tasks. The results are sometimes on par with or exceed the results of C-PLM; while being more efficient in terms of the number of parameters and training time. Interestingly, adapters inserted into a C-PLM generally yield better results than a traditional fine-tuned C-PLM. Our results open new directions to build more compact models for SE tasks.

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