  Code summarization generates brief natural language descriptions of source
code pieces, which can assist developers in understanding code and reduce
documentation workload. Recent neural models on code summarization are trained
and evaluated on large-scale multi-project datasets consisting of independent
code-summary pairs. Despite the technical advances, their effectiveness on a
specific project is rarely explored. In practical scenarios, however,
developers are more concerned with generating high-quality summaries for their
working projects. And these projects may not maintain sufficient documentation,
hence having few historical code-summary pairs. To this end, we investigate
low-resource project-specific code summarization, a novel task more consistent
with the developers' requirements. To better characterize project-specific
knowledge with limited training samples, we propose a meta transfer learning
method by incorporating a lightweight fine-tuning mechanism into a
meta-learning framework. Experimental results on nine real-world projects
verify the superiority of our method over alternative ones and reveal how the
project-specific knowledge is learned.
