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

delete2024-02-01
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OA
AI
黄箐 (Qing Huang)
Z
Zishuai Li
Z
Zhenchang Xing
左正康 (Zhengkang Zuo) *
X
Xin Peng
X
Xiwei Xu
Q
Qinghua Lu
DOI:10.1109/TSE.2023.3346954delete
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Abstract

Abstract

En 中文
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.
Keywords:
Pragmatics
Behavioral sciences
Semantics
Decision trees
Knowledge graphs
Java
Extensibility
Developers' API need
knowledge graph
human-AI dialogue
API recommendation
multi-round question answering

Journal

IEEE Transactions on Software Engineering cover
IEEE Transactions on Software Engineering
IF:
5.6
Papers:
2.8K
Citations:
1.1W

Organization

J
Jiangxi Normal University
Scholars:
6.9K
Papers: 4.7K
Citations: 8.8K
F
fudan university
Scholars:
11.6W
Papers: 7.7W
Citations: 121
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