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PyART: Python API Recommendation in Real-Time.

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Title: PyART: Python API Recommendation in Real-Time.
Authors: Xincheng He1 xinchenghe2016@gmail.com, Lei Xu1 xlei@nju.edu.cn, Xiangyu Zhang2 xyzhang@cs.purdue.edu, Rui Hao1 rui.hao.gm@gmail.com, Yang Feng1 fengyang@nju.edu.cn, Baowen Xu1 bwxu@nju.edu.cn
Source: ICSE: International Conference on Software Engineering. 5/22/2021, p1634-1645. 12p.
Subjects: Dylan (Computer program language), Python programming language, Artificial intelligence, Software engineering, Computer software packaging
Abstract: API recommendation in real-time is challenging for dynamic languages like Python. Many existing API recommendation techniques are highly effective, but they mainly support static languages. A few Python IDEs provide API recommendation functionalities based on type inference and training on a large corpus of Python libraries and third-party libraries. As such, they may fail to recommend or make poor recommendations when type information is missing or target APIs are project-specific. In this paper, we propose a novel approach, PyART, to recommending APIs for Python programs in real-time. It features a light-weight analysis to derive so-called optimistic data-flow, which is neither sound nor complete, but simulates the local data-flow information humans can derive. It extracts three kinds of features: data-flow, token similarity, and token co-occurrence, in the context of the program point where a recommendation is solicited. A predictive model is trained on these features using the Random Forest algorithm. Evaluation on 8 popular Python projects demonstrates that PyART can provide effective API recommendations. When historic commits can be leveraged, which is the target scenario of a state-of-the-art tool ARIREC, our average top-1 accuracy is over 50% and average top-10 accuracy over 70%, outperforming APIREC and Intellicode (i.e., the recommendation component in Visual Studio) by 28.48%-39.05% for top-1 accuracy and 24.41%-30.49% for top-10 accuracy. In other applications such as when historic comments are not available and cross-project recommendation, PyART also shows better overall performance. The time to make a recommendation is less than a second on average, satisfying the real-time requirement. [ABSTRACT FROM AUTHOR]
Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="AR" term="%22Xincheng+He%22">Xincheng He</searchLink><relatesTo>1</relatesTo><i> xinchenghe2016@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Lei+Xu%22">Lei Xu</searchLink><relatesTo>1</relatesTo><i> xlei@nju.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Xiangyu+Zhang%22">Xiangyu Zhang</searchLink><relatesTo>2</relatesTo><i> xyzhang@cs.purdue.edu</i><br /><searchLink fieldCode="AR" term="%22Rui+Hao%22">Rui Hao</searchLink><relatesTo>1</relatesTo><i> rui.hao.gm@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Yang+Feng%22">Yang Feng</searchLink><relatesTo>1</relatesTo><i> fengyang@nju.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Baowen+Xu%22">Baowen Xu</searchLink><relatesTo>1</relatesTo><i> bwxu@nju.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22ICSE%3A+International+Conference+on+Software+Engineering%22">ICSE: International Conference on Software Engineering</searchLink>. 5/22/2021, p1634-1645. 12p.
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  Data: API recommendation in real-time is challenging for dynamic languages like Python. Many existing API recommendation techniques are highly effective, but they mainly support static languages. A few Python IDEs provide API recommendation functionalities based on type inference and training on a large corpus of Python libraries and third-party libraries. As such, they may fail to recommend or make poor recommendations when type information is missing or target APIs are project-specific. In this paper, we propose a novel approach, PyART, to recommending APIs for Python programs in real-time. It features a light-weight analysis to derive so-called optimistic data-flow, which is neither sound nor complete, but simulates the local data-flow information humans can derive. It extracts three kinds of features: data-flow, token similarity, and token co-occurrence, in the context of the program point where a recommendation is solicited. A predictive model is trained on these features using the Random Forest algorithm. Evaluation on 8 popular Python projects demonstrates that PyART can provide effective API recommendations. When historic commits can be leveraged, which is the target scenario of a state-of-the-art tool ARIREC, our average top-1 accuracy is over 50% and average top-10 accuracy over 70%, outperforming APIREC and Intellicode (i.e., the recommendation component in Visual Studio) by 28.48%-39.05% for top-1 accuracy and 24.41%-30.49% for top-10 accuracy. In other applications such as when historic comments are not available and cross-project recommendation, PyART also shows better overall performance. The time to make a recommendation is less than a second on average, satisfying the real-time requirement. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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        Value: 10.1109/ICSE43902.2021.00145
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        Text: English
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      – SubjectFull: Python programming language
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      – SubjectFull: Software engineering
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      – SubjectFull: Computer software packaging
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      – TitleFull: PyART: Python API Recommendation in Real-Time.
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              Text: 5/22/2021
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