Source File Tracking Localization: A Fault Localization Method for Deep Learning Frameworks-Reference-Cited by-同舟云学术

Source File Tracking Localization: A Fault Localization Method for Deep Learning Frameworks

Published:2023-11-09 Issue:22 Volume:12 Page:4579
ISSN:2079-9292
Container-title:Electronics
language:en
Short-container-title:Electronics

Author:

Ma Zhenshu¹,Yang Bo¹,Zhang Yuhang¹

Affiliation:

1. School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China

Abstract

Deep learning has been widely used in computer vision, natural language processing, speech recognition, and other fields. If there are errors in deep learning frameworks, such as missing module errors and GPU/CPU result discrepancy errors, it will cause many application problems. We propose a source-based fault location method, SFTL (Source File Tracking Localization), to improve the fault location efficiency of these two types of errors in deep learning frameworks. We screened 3410 crash reports on GitHub and conducted fault location experiments based on those reports. The experimental results show that the SFTL method has a high accuracy, which can help deep learning framework developers quickly locate faults and improve the stability and reliability of models.

Funder

National Key R&D Program of China

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

Link

https://www.mdpi.com/2079-9292/12/22/4579/pdf

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