Checker Bug Detection and Repair in Deep Learning Libraries
Nima Shiri Harzevili, Mohammad Mahdi Mohajer, Jiho Shin, Moshi Wei,, Gias Uddin, Jinqiu Yang, Junjie Wang, Song Wang, Zhen Ming (Jack) Jiang,, Nachiappan Nagappan

TL;DR
This paper conducts the first comprehensive analysis of checker bugs in deep learning libraries, introduces TensorGuard for automated detection and repair using LLMs, and demonstrates its high effectiveness and potential for improving DL library reliability.
Contribution
It provides the first large-scale study of DL checker bugs, analyzes their root causes, and proposes TensorGuard, a novel LLM-based tool for detecting and fixing these bugs.
Findings
TensorGuard achieves 94.51% recall with Chain of Thought prompting.
TensorGuard's patch accuracy is 11.1%, surpassing the baseline by 2%.
Detected 64 new checker bugs in JAX using TensorGuard.
Abstract
Checker bugs in Deep Learning (DL) libraries are critical yet not well-explored. These bugs are often concealed in the input validation and error-checking code of DL libraries and can lead to silent failures, incorrect results, or unexpected program behavior in DL applications. Despite their potential to significantly impact the reliability and performance of DL-enabled systems built with these libraries, checker bugs have received limited attention. We present the first comprehensive study of DL checker bugs in two widely-used DL libraries, i.e., TensorFlow and PyTorch. Initially, we automatically collected a dataset of 2,418 commits from TensorFlow and PyTorch repositories on GitHub from Sept. 2016 to Dec. 2023 using specific keywords related to checker bugs. Through manual inspection, we identified 527 DL checker bugs. Subsequently, we analyzed these bugs from three perspectives,…
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Taxonomy
TopicsMachine Learning and Data Classification · Advanced Neural Network Applications · Advanced Data Storage Technologies
