Poirot: Deep Learning for API Misuse Detection
Document Type
Conference Proceeding
Publication Date
4-14-2024
Abstract
API misuses refer to incorrect usages that violate the usage constraints of API elements, potentially leading to issues such as runtime errors, exceptions, program crashes, and security vulnerabilities. Existing mining-based approaches for API misuse detection face challenges in accuracy, particularly in distinguishing infrequent from invalid usage. This limitation stems from the necessity to set predefined thresholds for frequent API usage patterns, resulting in potential misclassification of alternative usages. This paper introduces Poirot, a learning-based approach that mitigates the need for predefined thresholds. Leveraging Labeled, Graph-based Convolutional Networks, Poirot learns embeddings for API usages, capturing key features and enhancing API misuse detection. Preliminary evaluation on an API misuse benchmark demonstrates that Poirot achieves a relative improvement of 1.37-10.36X in F-score compared to state-of-the-art API misuse detection techniques.
Identifier
85194876048 (Scopus)
ISBN
[9798400705021]
Publication Title
Proceedings - International Conference on Software Engineering
External Full Text Location
https://doi.org/10.1145/3639478.3643080
ISSN
02705257
First Page
302
Last Page
303
Grant
CNS-2120386
Fund Ref
National Science Foundation
Recommended Citation
Li, Yi; Nguyen, Tien N.; Wang, Shaohua; and Yadavally, Aashish, "Poirot: Deep Learning for API Misuse Detection" (2024). Faculty Publications. 501.
https://digitalcommons.njit.edu/fac_pubs/501
