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RASOP: An API Recommendation Method Based on Word Embedding Technology

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Artificial Intelligence Algorithms and Applications (ISICA 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1205))

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Abstract

Users’ demand for the function of the software is increasingly affluent, and the scale of software is getting larger and larger. The structure of software presents the characteristics of complexity. In the process of software development, developers are likely to face a lot of difficulties, so they need to query the appropriate APIs. However, finding the right APIs can be time-consuming and laborious. It’s especially difficult for developers who don’t have much programming experience. In this paper, to solve the problems developers may face in the actual development process and improve the development efficiency, we propose RASOP (Recommendation APIs by Stack Overflow posts and Java Packages), an API recommendation approach leveraging word embedding technique and the information crawling from Stack Overflow posts and Java core packages, to recommend appropriate APIs for developers. Furthermore, RASOP also provides developers with label words, similar questions and relevant code. To evaluate the effectiveness of RASOP, we decided to analyze our system by simulating an instance. By testing a problem encountered during development, the API and tags and other recommendations from the RASOP output can indeed solve our problem. RASOP shows great results not only in the effect of API recommendation but also in the content of practicability.

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Acknowledgements

This work is supported partially by Natural Science Foundation of China under Grants no. 61602267, partially by the Open Project of Key Laboratoriesm of Ministry of Industry and Information Technology for Software Development and Verification Technology of High Safety Systems at Nanjing University of Aeronautics and Astronautics under Grants no. NJ2018014, partially by Natural Science Foundation of Nantong University under Grants no. 15Z14.

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Authors and Affiliations

  1. School of Information Science and Technology, Nantong University, Nantong, Jiangsu, China

    Bin Zhang, Lei Jin & Wanzhi Wen

  2. Key Laboratory of Safety-Critical Software, Nanjing University of Aeronautics and Astronautics, Ministry of Industry and Information Technology, Nanjing, China

    Wanzhi Wen

  3. Modern Educational Technology Centre, Nantong University, Nantong, Jiangsu, China

    Lihua Sheng

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  1. Bin Zhang
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  2. Lihua Sheng
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  3. Lei Jin
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  4. Wanzhi Wen
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Corresponding author

Correspondence to Wanzhi Wen .

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Editors and Affiliations

  1. South China Agricultural University, Guangzhou, China

    Kangshun Li

  2. Jiangxi University of Science and Technology, Ganzhou, China

    Wei Li

  3. South China Agricultural University, Guangzhou, China

    Hui Wang

  4. The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Yong Liu

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Zhang, B., Sheng, L., Jin, L., Wen, W. (2020). RASOP: An API Recommendation Method Based on Word Embedding Technology. In: Li, K., Li, W., Wang, H., Liu, Y. (eds) Artificial Intelligence Algorithms and Applications. ISICA 2019. Communications in Computer and Information Science, vol 1205. Springer, Singapore. https://doi.org/10.1007/978-981-15-5577-0_21

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  • DOI: https://doi.org/10.1007/978-981-15-5577-0_21

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-5576-3

  • Online ISBN: 978-981-15-5577-0

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