Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2023

Abstract

While having options could be liberating, too many options could lead to the sub-optimal solution being chosen. This is not an exception in the software engineering domain. Nowadays, API has become imperative in making software developers' life easier. APIs help developers implement a function faster and more efficiently. However, given the large number of open-source libraries to choose from, choosing the right APIs is not a simple task. Previous studies on API recommendation leverage natural language (query) to identify which API would be suitable for the given task. However, these studies only consider one source of input, i.e., GitHub or Stack Overflow, independently. There are no existing approaches that utilize Stack Overflow to help generate better API sequence recommendations from queries obtained from GitHub. Therefore, in this study, we aim to provide a framework that could improve the result of the API sequence recommendation by leveraging information from Stack Overflow. In this work, we propose Picaso, which leverages contrastive learning to train a sentence embedding model and a cross-encoder model to build a classification model in order to find a semantically similar Stack Overflow post given an annotation (i.e., code comment). Subsequently, Picaso then uses the Stack Overflow's title as a query expansion. Picaso then uses the extended queries to fine-tune a CodeBERT, resulting in an API sequence generation model. Based on our experiments, we found that incorporating the Stack Overflow information into CodeBERT would improve the performance of API sequence generation's BLEU-4 score by 10.8%.

Keywords

API recommendation, Multi-source analytic, Multi-Sources, Pre-trained model, Query expansion, Sequence generation, Software developer, Software engineering domain, Stack overflow, Suboptimal solution

Discipline

Databases and Information Systems | Software Engineering

Research Areas

Data Science and Engineering

Publication

Proceedings of the 20th IEEE/ACM International Conference on Mining Software Repositories, Melbourne, Australia, 2023 May 15-16

First Page

92

Last Page

103

ISBN

9798350311846

Identifier

10.1109/MSR59073.2023.00025

Publisher

IEEE

City or Country

New Jersey

Additional URL

https://doi.org/10.1109/MSR59073.2023.00025

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