论文标题
代码摘要的元学习
Meta Learning for Code Summarization
论文作者
论文摘要
源代码摘要是为编程语言代码段生成高级自然语言描述的任务。该任务的当前神经模型在其体系结构和他们考虑的代码方面有所不同。在本文中，我们表明，用于代码摘要的三个SOTA模型在很大程度上是差异的大型代码库中的子集。这种互补性激发了模型组合：我们提出了三个元模型，这些元模型选择给定代码段的最佳候选摘要。两种神经模型在最佳单个模型的性能上显着改善，在代码段的数据集中获得了2.1个BLEU点，其中至少一个单个模型获得了非零的BLEU。
Source code summarization is the task of generating a high-level natural language description for a segment of programming language code. Current neural models for the task differ in their architecture and the aspects of code they consider. In this paper, we show that three SOTA models for code summarization work well on largely disjoint subsets of a large code-base. This complementarity motivates model combination: We propose three meta-models that select the best candidate summary for a given code segment. The two neural models improve significantly over the performance of the best individual model, obtaining an improvement of 2.1 BLEU points on a dataset of code segments where at least one of the individual models obtains a non-zero BLEU.
