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Validation and CI [clear filter]
Tuesday, April 2

11:00am GMT+07

BKK19-217 Experiences and lessons we learned using kselftest and potential improvements.
Over the past years, we ran the kernel selftests as part of the LTS release testing. During that time, we learned things, fixed things and created a wish list of work we want to tackle. This session is about what we learned and where we are heading.

avatar for Anders Roxell

Anders Roxell

Software Engineer, Linaro
Anders hates running tests and therefore he loves automating them. He has been working with Linux kernels for telecommunication (e.g. base stations, media gateways) as well as various drivers and RTOS’s for automotive systems (e.g. engine-, gearbox-platforms). He has also experience... Read More →

Tuesday April 2, 2019 11:00am - 11:25am GMT+07
Keynote Room (World Ballroom BC)

12:30pm GMT+07

BKK19-219 KernelCI New Generation
KernelCI generates daily an average of two thousand Linux Kernel builds, submitted to several labs across planet to mainly check if system booting works correctly. Therefore tons of data are created and displayed on a frontend interface for further use. Current KernelCI requires significant technical effort changing visualizations, including builds and jobs listings. The KernelCI New Generation seeks to apply state-of-the-art data collection and data visualization tools as an alternative frontend to KernelCI. Our focus is on an instance of ElasticSearch, Logstash, and Kibana collecting and displaying live KernelCI data. All the tooling used to this project will be available.

avatar for Charles Daniel De Oliveira

Charles Daniel De Oliveira

Software Engineer, Linaro
Joined Linaro in 2018 working in various projects for the company.

Tuesday April 2, 2019 12:30pm - 12:55pm GMT+07
Keynote Room (World Ballroom BC)
Thursday, April 4

9:00am GMT+07

BKK19-416 Automating test results analysis using neural networks
Test results triage is the most time-consuming step currently in Texas Instruments CI cycle. Embedded tests are more susceptible to failures caused by external factors due to test setup complexity. A typical embedded test contains multiple points of failure.
This work uses a neural network to classify test results automatically and to detect 'real' regressions. It helps scaling TI's validation cycle by automating the most time-consuming step.

avatar for Carlos Hernandez

Carlos Hernandez

Eng Mgr, Texas Instruments
15+ years of experience in the embedded industry.Co-author of Opentest validation framewrok.Linux Integration & Validation manager at TI since 2015.

Thursday April 4, 2019 9:00am - 9:25am GMT+07
Session Room 3 (Lotus 10)

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