Intelligent Identification of Simultaneous Faults of Automotive Software Systems Under Noisy and Imbalanced Data Using Ensemble LSTM and Random Forest-Reference-Cited by-同舟云学术

Intelligent Identification of Simultaneous Faults of Automotive Software Systems Under Noisy and Imbalanced Data Using Ensemble LSTM and Random Forest

Published:2023 Issue: Volume:11 Page:140022-140040
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Abboush Mohammad¹^ORCID,Knieke Christoph¹^ORCID,Rausch Andreas¹

Affiliation:

1. Institute for Software and Systems Engineering, Clausthal University of Technology, Clausthal-Zellerfeld, Germany

Funder

Open Access Publishing Fund of Clausthal University of Technology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10348584.pdf?arnumber=10348584

Reference74 articles.

1. Classification of driver-assistance systems according to their impact on road safety and traffic efficiency

2. Feature dependencies in automotive software systems: Extent, awareness, and refactoring

3. Testing automotive embedded systems under X-in-the-loop setups

4. Testing embedded software: A survey of the literature

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