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.

中文翻译：

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通过知识感知的人机对话协助回答不确定、不明确的 API 查询

开发者的API需求应该更加务实，比如寻求具有启发性、可解释性、可扩展性的API，而不是所谓的最佳结果。现有的 API 搜索研究无法满足这些实用需求，因为它们只关注查询 API 相关性。这就需要重点关注增强整个查询过程，从查询定义到查询细化，再到意图澄清，再到查询结果，从而促进对结果的发散思维。本文设计了一种新颖的知识感知人类人工智能对话代理（KAHAID），它引导开发人员通过多轮问答来澄清不确定的、不明确的查询，并为澄清的查询推荐具有相关解释和扩展建议的API（例如、替代、协作或相反功能的 API）。我们系统地评估 KAHAID。在人机对话过程中，实现了问题选项的高度多样性（任意两个选项之间的平均多样性为74.9%），并能够用更少的对话轮次（平均不超过3轮）引导开发者找到API ）。对于 API 推荐，KAHAID 的 MRR 和 MAP 分别为 0.769 和 0.794，在 MRR 上比最先进的 API 搜索方法 BIKER 和 CLEAR 至少高出 47%，在 MAP 上高出 226.7%。对于知识扩展，KAHAID 获得了 0.815 和 0.864 的 MRR 和 MAP，在 MRR 上超过了最先进的查询澄清方法至少 42%，在 MAP 上超过了 45.2%。作为同类首创，KAHAID 打开了整合 API 研究的即时响应能力和社会技术信息搜索的交互、澄清、解释和可扩展能力的大门。