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Context-aware API recommendation using tensor factorization

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Abstract

An activity constantly engaged by most programmers in coding is to search for appropriate application programming interfaces (APIs). Contextual information is widely recognized to play a crucial role in effective API recommendation, but it is largely overlooked in practice. In this paper, we propose context-aware API recommendation using tensor factorization (CARTF), a novel API recommendation approach in considering programmers’ working context. To this end, we use tensors to explicitly represent the query-API-context triadic relation. When a new query is made, CARTF harnesses word embeddings to retrieve similar user queries, based on which a third-order tensor is constructed. CARTF then applies non-negative tensor factorization to complete missing values in the tensor and the Smith-Waterman algorithm to identify the most matched context. Finally, the ranking of the candidate APIs can be derived based on which API sequences are recommended. Our evaluation confirms the effectiveness of CARTF for class-level and method-level API recommendations, outperforming state-of-the-art baseline approaches against a number of performance metrics, including SuccessRate, Precision, and Recall.

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Acknowledgements

This work was partially supported by National Natural Science Foundation of China (Grant Nos. 61972197, 61802179), Collaborative Innovation Center of Novel Software Technology and Industrialization, and Qing Lan Project. Taolue CHEN is partially supported by Birkbeck BEI School Project (EFFECT), National Natural Science Foundation of China (Grant No. 61872340), Guangdong Science and Technology Department (Grant No. 2018B010107004), and Natural Science Foundation of Guangdong Province, China (Grant No. 2019A1515011689).

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Authors and Affiliations

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Yu Zhou, Chen Chen & Yongchao Wang

  2. Birkbeck, University of London, London, WC1E 7HX, UK

    Tingting Han & Taolue Chen

  3. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China

    Yu Zhou & Taolue Chen

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  1. Yu Zhou
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  2. Chen Chen
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  5. Taolue Chen
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Correspondence to Taolue Chen.

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Tables S1–S4. The supporting information is available online at info.scichina.com and link.springer.com. The supporting materials are published as submitted, without typesetting or editing. The responsibility for scientific accuracy and content remains entirely with the authors.

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Zhou, Y., Chen, C., Wang, Y. et al. Context-aware API recommendation using tensor factorization. Sci. China Inf. Sci. 66, 122101 (2023). https://doi.org/10.1007/s11432-021-3529-9

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