Knowledge-guided large language models are trustworthy API recommenders

Application Programming Interface (API) recommendation aims to recommend APIs for developers that meet their functional requirements, which can compensate for developers’ lack of API knowledge. In team-based software development, developers often need to implement functionality based on specific interface parameter types predefined by the software architect. Therefore, we propose API Recommendation under specific Interface Parameter Types (APIRIP), a special variant of the API recommendation task that requires the recommended APIs to conform to the interface parameter types. To realize APIRIP, we enlist the support of Large Language Models (LLMs). However, LLMs are susceptible to the phenomenon known as hallucination, wherein they may recommend untrustworthy API sequences. Instances of this include recommending fictitious APIs, APIs whose calling conditions cannot be satisfied, or API sequences that fail to conform to the interface parameter types. To mitigate these issues, we propose a Knowledge-guided framework for LLM-based API Recommendation (KG4LLM), which incorporates knowledge-guided data augmentation and beam search. The core idea of KG4LLM is to leverage API knowledge derived from the Java Development Kit (JDK) documentation to enhance the trustworthiness of LLM-generated recommendations. Experimental results demonstrate that KG4LLM can improve the trustworthiness of recommendation results provided by LLM and outperform advanced LLMs in the APIRIP task.