Automated trainability evaluation for smart software functions

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F19%3A10408457" target="_blank" >RIV/00216208:11320/19:10408457 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1109/ASE.2019.00096" target="_blank" >https://doi.org/10.1109/ASE.2019.00096</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ASE.2019.00096" target="_blank" >10.1109/ASE.2019.00096</a>

Result language

angličtina

Original language name

Automated trainability evaluation for smart software functions

Original language description

More and more software-intensive systems employ machine learning and runtime optimization to improve their functionality by providing advanced features (e. g. personal driving assistants or recommendation engines). Such systems incorporate a number of smart software functions (SSFs) which gradually learn and adapt to the users&apos; preferences. A key property of SSFs is their ability to learn based on data resulting from the interaction with the user (implicit and explicit feedback)-which we call trainability. Newly developed and enhanced features in a SSF must be evaluated based on their effect on the trainability of the system. Despite recent approaches for continuous deployment of machine learning systems, trainability evaluation is not yet part of continuous integration and deployment (CID) pipelines. In this paper, we describe the different facets of trainability for the development of SSFs. We also present our approach for automated trainability evaluation within an automotive CID framework which proposes to use automated quality gates for the continuous evaluation of machine learning models. The results from our indicative evaluation based on real data from eight BMW cars highlight the importance of continuous and rigorous trainability evaluation in the development of SSFs.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings - 2019 34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019

ISBN

978-1-72812-508-4

ISSN

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e-ISSN

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Number of pages

4

Pages from-to

998-1001

Publisher name

IEEE

Place of publication

Neuveden

Event location

San Diego, United States

Event date

Nov 10, 2019

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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