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A Closed Loop Method for Vehicle Instrument Cluster Test Automation General Motors LLC

SAE Technical Papers (1906-current) Available online

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Format:
Book
Conference/Event
Author/Creator:
G, G., author.
Contributor:
Ramu, Arunkumar
Shastry, Raghuram
Conference Name:
WCX SAE World Congress Experience (2019-04-09 : Detroit, Michigan, United States)
Language:
English
Physical Description:
1 online resource cm
Place of Publication:
Warrendale, PA SAE International 2019
Summary:
AbstractInstrument Panel Cluster (IPC), is a key ECU in vehicles. As IPC is a visual product, testing the software features of IPC is highly manual effort. Software Testing constitutes for approximately 35% of the total Software Development Life Cycle (SDLC). High focus on quick to market, shorter SDLC coupled with manual validation environment poses a challenge of increasing testing efficiency and improving software quality. This challenge drove the need to investigate a solution to automate the testing process and cut down the huge manual effort that goes into validating an Instrument Panel Cluster (IPC) software. The proposed intrusive and non-intrusive approaches to automate the testing process of IPC software employs a Frame Grabbing technique for the former approach and a Camera based technique for the latter. Both the approaches are robust, reliable, and scalable and covers the major portion of Vehicle Instrument cluster test scenarios. The paper captures the results, advantages and the constraints in the implementation of both the approaches. The comprehensive evaluation of the proposed techniques will help in identifying the right approach for the need, satisfying necessary criteria for automation. The paper aims to provide a best possible solution to optimize the tedious efforts which goes into validating an Instrument Panel Cluster (IPC) software and in turn enables a flawless software delivery within the stipulated timeline
Notes:
Vendor supplied data
Publisher Number:
2019-01-1250
Access Restriction:
Restricted for use by site license

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