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View all- Yang HNong YZhang TLuo XCai H(2024)Learning to Detect and Localize Multilingual BugsProceedings of the ACM on Software Engineering10.1145/36608041:FSE(2190-2213)Online publication date: 12-Jul-2024
PTM-APIRec: Leveraging Pre-trained Models of Source Code in API Recommendation
Authors: 
Zhihao Li, Chuanyi Li, Ze Tang, Wanhong Huang, Jidong Ge, Bin Luo, Vincent Ng, Ting Wang, Yucheng Hu, Xiaopeng ZhangAuthors Info & Claims
Zhihao Li, Chuanyi Li, Ze Tang, Wanhong Huang, Jidong Ge, Bin Luo, Vincent Ng, Ting Wang, Yucheng Hu, Xiaopeng ZhangAuthors Info & ClaimsArticle No.: 72, Pages 1 - 30
Abstract
Recommending APIs is a practical and essential feature of IDEs. Improving the accuracy of API recommendations is an effective way to improve coding efficiency. With the success of deep learning in software engineering, the state-of-the-art (SOTA) performance of API recommendation is also achieved by deep-learning-based approaches. However, existing SOTAs either only consider the API sequences in the code snippets or rely on complex operations for extracting hand-crafted features, all of which have potential risks in under-encoding the input code snippets and further resulting in sub-optimal recommendation performance. To this end, this article proposes to utilize the code understanding ability of existing general code Pre-Training Models to fully encode the input code snippet to improve the accuracy of API Recommendation, namely, PTM-APIRec. To ensure that the code semantics of the input are fully understood and the API recommended actually exists, we use separate vocabularies for the input code snippet and the APIs to be predicted. The experimental results on the JDK and Android datasets show that PTM-APIRec surpasses existing approaches. Besides, an effective way to improve the performance of PTM-APIRec is to enhance the pre-trained model with more pre-training data (which is easier to obtain than API recommendation datasets).
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Index Terms
- PTM-APIRec: Leveraging Pre-trained Models of Source Code in API Recommendation
Index terms have been assigned to the content through auto-classification.
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Publication History
Published: 15 March 2024
Online AM: 14 November 2023
Accepted: 26 October 2023
Revised: 05 September 2023
Received: 23 May 2023
Published in TOSEM Volume 33, Issue 3
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View all- Yang HNong YZhang TLuo XCai H(2024)Learning to Detect and Localize Multilingual BugsProceedings of the ACM on Software Engineering10.1145/36608041:FSE(2190-2213)Online publication date: 12-Jul-2024
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