Demystifying and Detecting Misuses of Deep Learning APIs

Moshi Wei, Nima Shiri Harzevili, Yuekai Huang, Jinqiu Yang, Junjie Wang, Song Wang

Abstract: Deep Learning (DL) libraries have significantly impacted various domains in computer science over the last decade. However, developers often face challenges when using the DL APIs, as the development paradigm of DL applications differs greatly from traditional software development. Existing studies on API misuse mainly focus on traditional software, leaving a gap in understanding API misuse within DL APIs. To address this gap, we present the first comprehensive study of DL API misuse in TensorFlow and PyTorch. Specifically, we first collected a dataset of 4,224 commits from the top 200 most-starred projects using these two libraries and manually identified 891 API misuses. We then investigated the characteristics of these misuses from three perspectives, i.e., types, root causes, and symptoms. We have also conducted an evaluation to assess the effectiveness of the current state-of-the-art API misuse detector on our 891 confirmed API misuses. Our results confirmed that the state-of-the-art API misuse detector is ineffective in detecting DL API misuses. To address the limitations of existing API misuse detection for DL APIs, we propose LLMAPIDet, which leverages Large Language Models (LLMs) for DL API misuse detection and repair. We build LLMAPIDet by prompt-tuning a chain of ChatGPT prompts on 600 out of 891 confirmed API misuses and reserve the rest 291 API misuses as the testing dataset. Our evaluation shows that LLMAPIDet can detect 48 out of the 291 DL API misuses while none of them can be detected by the existing API misuse detector. We further evaluate LLMAPIDet on the latest versions of 10 GitHub projects. The evaluation shows that LLMAPIDet can identify 119 previously unknown API misuses and successfully fix 46 of them.