Recommending APIs is a practical and essential feature of IDEs. Improving the accuracy of API recommendations is an effective way to improve coding efficiency. With the success of deep learning in software engineering, the state-of-the-art (SOTA) performance of API recommendation is also achieved by deep-learning-based approaches. However, existing SOTAs either only consider the API sequences in the code snippets or rely on complex operations for extracting hand-crafted features, all of which have potential risks in under-encoding the input code snippets and further resulting in sub-optimal recommendation performance. To this end, this article proposes to utilize the code understanding ability of existing general code Pre-Training Models to fully encode the input code snippet to improve the accuracy of API Recommendation, namely, PTM-APIRec. To ensure that the code semantics of the input are fully understood and the API recommended actually exists, we use separate vocabularies for the input code snippet and the APIs to be predicted. The experimental results on the JDK and Android datasets show that PTM-APIRec surpasses existing approaches. Besides, an effective way to improve the performance of PTM-APIRec is to enhance the pre-trained model with more pre-training data (which is easier to obtain than API recommendation datasets).

推荐API是IDE的一个实用且必不可少的功能。提高API推荐的准确性是提高编码效率的有效途径。随着深度学习在软件工程中的成功，基于深度学习的方法也实现了 API 推荐的最先进（SOTA）性能。然而，现有的 SOTA 要么只考虑代码片段中的 API 序列，要么依赖复杂的操作来提取手工制作的特征，所有这些都存在对输入代码片段编码不足并进一步导致推荐性能次优的潜在风险。为此，本文提出利用现有通用代码的代码理解能力磷关于-时间下雨中号模型对输入代码片段进行完全编码，以提高准确性应用程序编程接口 记录推荐，即PTM-APIRec。为了确保输入的代码语义被完全理解并且推荐的 API 确实存在，我们对输入代码片段和要预测的 API 使用单独的词汇表。在 JDK 和 Android 数据集上的实验结果表明 PTM-APIRec 超越了现有方法。此外，提高PTM-APIRec性能的有效方法是使用更多的预训练数据（比API推荐数据集更容易获得）来增强预训练模型。