Answering Uncertain, Under-Specified API Queries Assisted by Knowledge-Aware Human-AI Dialogue

Developers' API needs should be more pragmatic, such as seeking suggestive, explainable, and extensible APIs rather than the so-called best result. Existing API search research cannot meet these pragmatic needs because they are solely concerned with query-API relevance. This necessitates a focus on enhancing the entire query process, from query definition to query refinement through intent clarification to query results promoting divergent thinking about results. This paper designs a novel Knowledge-Aware Human-AI Dialog agent (KAHAID) which guides the developer to clarify the uncertain, under-specified query through multi-round question answering and recommends APIs for the clarified query with relevance explanation and extended suggestions (e.g., alternative, collaborating or opposite-function APIs). We systematically evaluate KAHAID. In terms of human-AI dialogue process, it achieves a high diversity of question options (the average diversity between any two options is 74.9%) and the ability to guide developers to find APIs using fewer dialogue rounds (no more than 3 rounds on average). For API recommendation, KAHAID achieves an MRR and MAP of 0.769 and 0.794, outperforming state-of-the-art API search approaches BIKER and CLEAR by at least 47% in MRR and 226.7% in MAP. For knowledge extension, KAHAID obtains an MRR and MAP of 0.815 and 0.864, surpassing state-of-the-art query clarification approaches by at least 42% in MRR and 45.2% in MAP. As the first of its kind, KAHAID opens the door to integrating the immediate response capability of API research and the interaction, clarification, explanation, and extensibility capability of social-technical information seeking.