Publication Type

Journal Article

Version

acceptedVersion

Publication Date

2-2022

Abstract

A common cause of bugs and vulnerabilities are the violations of usage constraints associated with Application Programming Interfaces (APIs). API misuses are common in software projects, and while there have been techniques proposed to detect such misuses, studies have shown that they fail to reliably detect misuses while reporting many false positives. One limitation of prior work is the inability to reliably identify correct patterns of usage. Many approaches confuse a usage pattern’s frequency for correctness. Due to the variety of alternative usage patterns that may be uncommon but correct, anomaly detection-based techniques have limited success in identifying misuses. We address these challenges and propose ALP (Actively Learned Patterns), reformulating API misuse detection as a classification problem. After representing programs as graphs, ALP mines discriminative subgraphs. While still incorporating frequency information, through limited human supervision, we reduce the reliance on the assumption relating frequency and correctness. The principles of active learning are incorporated to shift human attention away from the most frequent patterns. Instead, ALP samples informative and representative examples while minimizing labeling effort. In our empirical evaluation, ALP substantially outperforms prior approaches on both MUBench, an API Misuse benchmark, and a new dataset that we constructed from real-world software projects.

Keywords

API-Misuse Detection, Discriminative Subgraph Mining, Graph Classification, Active Learning

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

IEEE Transactions on Software Engineering

Volume

48

Issue

8

First Page

2761

Last Page

2781

ISSN

0098-5589

Identifier

10.1109/TSE.2021.3069978

Publisher

Institute of Electrical and Electronics Engineers

Additional URL

https://doi.org/10.1109/TSE.2021.3069978

Download

Share

COinS