Using Prompt Tuning to Identify Relevant API Knowledge from API Tutorial and Stack Overflow

API tutorials and Stack Overflow (SO) are crucial API learning resources. API tutorials help developers understand API usage in general contexts, while SO explains API usage in specific programming tasks. Using both API tutorials and SO provides more API knowledge. We treat a tutorial fragment or a SO Question and Answering (Q&A for short) pair as a knowledge item (KI for short). Discovering relevant KIs of the APIs helps developers understand and learn how to use APIs. However, existing relevant KIs identification approaches mainly focus on either API tutorials or SO. Furthermore, these approaches do not take into account code in KIs. In this paper, we propose PTIRK, a novel approach using prompt tuning to identify relevant KIs of API from both API tutorials and SO. Firstly, we extract textual descriptions and code snippets from KIs and match them with the API class name. Then, prompt tuning is used to tune BERT for text in KIs and CodeBERT for code in KIs. After that, we combine tuned models by using a gating fusion strategy. Finally, the relevant KIs of API can be identified by the trained model. We evaluate PTIRK on Java and Android datasets with 10,072 samples. Experimental results show that PTIRK outperforms state-of-the-art approaches on both datasets, and the user study further confirms its practical effectiveness.