research-article

PTM-APIRec: Leveraging Pre-trained Models of Source Code in API Recommendation

Authors:

Xiaopeng ZhangAuthors Info & Claims

ACM Transactions on Software Engineering and Methodology, Volume 33, Issue 3

Article No.: 72, Pages 1 - 30

https://doi.org/10.1145/3632745

Published: 15 March 2024 Publication History

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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Cited By

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
https://dl.acm.org/doi/10.1145/3660804
Kong XLv ZChen CChang HLi NZhang F(2024)Code Recommendation for Schema Evolution of Mimic Storage SystemsInternational Journal of Software Engineering and Knowledge Engineering10.1142/S0218194024500499(1-22)Online publication date: 28-Oct-2024
https://doi.org/10.1142/S0218194024500499

Index Terms

PTM-APIRec: Leveraging Pre-trained Models of Source Code in API Recommendation
1. Human-centered computing
  1. Ubiquitous and mobile computing
2. Software and its engineering
  1. Software creation and management
    1. Software development techniques
  2. Software notations and tools

Index terms have been assigned to the content through auto-classification.

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cover image ACM Transactions on Software Engineering and Methodology

ACM Transactions on Software Engineering and Methodology Volume 33, Issue 3

March 2024

943 pages

EISSN:1557-7392

DOI:10.1145/3613618

Editor:
Mauro Pezzé
USI Universitá della Svizzera italiana and SIT Schaffhausen Institute of Technology, Switzerland

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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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Cited By

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
https://dl.acm.org/doi/10.1145/3660804
Kong XLv ZChen CChang HLi NZhang F(2024)Code Recommendation for Schema Evolution of Mimic Storage SystemsInternational Journal of Software Engineering and Knowledge Engineering10.1142/S0218194024500499(1-22)Online publication date: 28-Oct-2024
https://doi.org/10.1142/S0218194024500499

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