  Pre-trained neural Language Models (PTLM), such as CodeBERT, are recently
used in software engineering as models pre-trained on large source code
corpora. Their knowledge is transferred to downstream tasks (e.g. code clone
detection) via fine-tuning. In natural language processing (NLP), other
alternatives for transferring the knowledge of PTLMs are explored through using
adapters, compact, parameter efficient modules inserted in the layers of the
PTLM. Although adapters are known to facilitate adapting to many downstream
tasks compared to fine-tuning the model that require retraining all of the
models' parameters -- which owes to the adapters' plug and play nature and
being parameter efficient -- their usage in software engineering is not
explored.
  Here, we explore the knowledge transfer using adapters and based on the
Naturalness Hypothesis proposed by Hindle et. al \cite{hindle2016naturalness}.
Thus, studying the bimodality of adapters for two tasks of cloze test and code
clone detection, compared to their benchmarks from the CodeXGLUE platform.
These adapters are trained using programming languages and are inserted in a
PTLM that is pre-trained on English corpora (N-PTLM). Three programming
languages, C/C++, Python, and Java, are studied along with extensive
experiments on the best setup used for adapters. Improving the results of the
N-PTLM confirms the success of the adapters in knowledge transfer to software
engineering, which sometimes are in par with or exceed the results of a PTLM
trained on source code; while being more efficient in terms of the number of
parameters, memory usage, and inference time. Our results can open new
directions to build smaller models for more software engineering tasks. We open
source all the scripts and the trained adapters.
