@inproceedings{8f16763e52cd408e959b545ba9d8f96f,
title = "SimilarAPI: Mining Analogical APIs for Library Migration",
abstract = "Establishing API mappings between libraries is a prerequisite step for library migration tasks. Manually establishing API mappings is tedious due to the large number of APIs to be examined, and existing methods based on supervised learning requires unavailable already-ported or functionality similar applications. Therefore, we propose an unsupervised deep learning based approach to embed both API usage semantics and API description (name and document) semantics into vector space for inferring likely analogical API mappings between libraries. We implement a proof-of-concept website SimilarAPI (https://similarapi.appspot.com) which can recommend analogical APIs for 583,501 APIs of 111 pairs of analogical Java libraries with diverse functionalities. Video: Https://youtu.be/EAwD6l24vLQ",
keywords = "Analogical API, Skip thoughts, Word embedding",
author = "Chunyang Chen",
note = "Publisher Copyright: {\textcopyright} 2020 ACM.; 42nd ACM/IEEE International Conference on Software Engineering: Companion, ICSE-Companion 2020 ; Conference date: 27-06-2020 Through 19-07-2020",
year = "2020",
month = oct,
doi = "10.1145/3377812.3382140",
language = "English",
series = "Proceedings - 2020 ACM/IEEE 42nd International Conference on Software Engineering: Companion, ICSE-Companion 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "37--40",
booktitle = "Proceedings - 2020 ACM/IEEE 42nd International Conference on Software Engineering",
}