Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

10-2024

Abstract

Developers rely on third-party library Application Programming Interfaces (APIs) when developing software. However, libraries typically come with assumptions and API usage constraints, whose violation results in API misuse. API misuses may result in crashes or incorrect behavior. Even though API misuse is a well-studied area, a recent study of API misuse of deep learning libraries showed that the nature of these misuses and their symptoms are different from misuses of traditional libraries, and as a result highlighted potential shortcomings of current misuse detection tools. We speculate that these observations may not be limited to deep learning API misuses but may stem from the data-centric nature of these APIs. Data-centric libraries often deal with diverse data structures, intricate processing workflows, and a multitude of parameters, which can make them inherently more challenging to use correctly. Therefore, understanding the potential misuses of these libraries is important to avoid unexpected application behavior. To this end, this paper contributes an empirical study of API misuses of five data-centric libraries that cover areas such as data processing, numerical computation, machine learning, and visualization. We identify misuses of these libraries by analyzing data from both Stack Overflow and GitHub. Our results show that many of the characteristics of API misuses observed for deep learning libraries extend to misuses of the data-centric library APIs we study. We also find that developers tend to misuse APIs from data-centric libraries, regardless of whether the API directive appears in the documentation. Overall, our work exposes the challenges of API misuse in data-centric libraries, rather than only focusing on deep learning libraries. Our collected misuses and their characterization lay groundwork for future research to help reduce misuses of these libraries.

Keywords

API misuse, data-centric libraries, empirical study

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

ESEM '24: Proceedings of the 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, Barcelona, October 24-25

First Page

245

Last Page

256

ISBN

9798400710476

Identifier

10.1145/3674805.3686685

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/3674805.3686685

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