IVVES (Industrial-Grade Verification and Validation of Evolving Systems)

Pekka Aho; Tanja E. J. Vos; Otto Sybrandi; Sorin Patrasoiu; Joona Oikarinen; Olivia Rodriguez Valdes; Lianne V. Hufkens

IVVES (Industrial-Grade Verification and Validation of Evolving Systems)

Pekka Aho, Tanja E. J. Vos, Otto Sybrandi, Sorin Patrasoiu, Joona Oikarinen, Olivia Rodriguez Valdes, Lianne V. Hufkens

Department of Computer Science

Research output: Contribution to conference › Paper › Academic

Abstract

Anincreasing numberofinformationsystemsarebasedonmachinelearning(ML)orartificialintelligence (AI). In some cases, the systems are adapting their behaviour during operation based on the data being gathered. This introduces new challenges for verification, validation and software testing. The traditional way of testing the systems during the development and before the deployment does not suffice anymore. IVVES (Industrial-Grade Verification and Validation of Evolving Systems) project aims to address these challenges by researching and developing methods to test ML and AI solutions and evolving systems, and using AI and ML to improve and automate development and testing. We summarise the results of the project at a high level, and provide more details on the research and collaboration related to scriptless end-to-end testing through graphical user interface.

Original language	English
Number of pages	8
Publication status	Published - 19 May 2022
Event	16th International Conference on Research Challenges in Information Science - Barcelona, Spain Duration: 17 May 2022 → 20 May 2022 https://www.rcis-conf.com/rcis2022/

Conference

Conference	16th International Conference on Research Challenges in Information Science
Abbreviated title	RCIS 2022
Country/Territory	Spain
City	Barcelona
Period	17/05/22 → 20/05/22
Internet address	https://www.rcis-conf.com/rcis2022/

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https://dblp.org/rec/conf/rcis/AhoVSPOVH22Licence: CC BY

Cite this

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abstract = "Anincreasing numberofinformationsystemsarebasedonmachinelearning(ML)orartificialintelligence (AI). In some cases, the systems are adapting their behaviour during operation based on the data being gathered. This introduces new challenges for verification, validation and software testing. The traditional way of testing the systems during the development and before the deployment does not suffice anymore. IVVES (Industrial-Grade Verification and Validation of Evolving Systems) project aims to address these challenges by researching and developing methods to test ML and AI solutions and evolving systems, and using AI and ML to improve and automate development and testing. We summarise the results of the project at a high level, and provide more details on the research and collaboration related to scriptless end-to-end testing through graphical user interface.",

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AU - Vos, Tanja E. J.

AU - Sybrandi, Otto

AU - Patrasoiu, Sorin

AU - Oikarinen, Joona

AU - Valdes, Olivia Rodriguez

AU - Hufkens, Lianne V.

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PY - 2022/5/19

Y1 - 2022/5/19

N2 - Anincreasing numberofinformationsystemsarebasedonmachinelearning(ML)orartificialintelligence (AI). In some cases, the systems are adapting their behaviour during operation based on the data being gathered. This introduces new challenges for verification, validation and software testing. The traditional way of testing the systems during the development and before the deployment does not suffice anymore. IVVES (Industrial-Grade Verification and Validation of Evolving Systems) project aims to address these challenges by researching and developing methods to test ML and AI solutions and evolving systems, and using AI and ML to improve and automate development and testing. We summarise the results of the project at a high level, and provide more details on the research and collaboration related to scriptless end-to-end testing through graphical user interface.

AB - Anincreasing numberofinformationsystemsarebasedonmachinelearning(ML)orartificialintelligence (AI). In some cases, the systems are adapting their behaviour during operation based on the data being gathered. This introduces new challenges for verification, validation and software testing. The traditional way of testing the systems during the development and before the deployment does not suffice anymore. IVVES (Industrial-Grade Verification and Validation of Evolving Systems) project aims to address these challenges by researching and developing methods to test ML and AI solutions and evolving systems, and using AI and ML to improve and automate development and testing. We summarise the results of the project at a high level, and provide more details on the research and collaboration related to scriptless end-to-end testing through graphical user interface.

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T2 - 16th International Conference on Research Challenges in Information Science

Y2 - 17 May 2022 through 20 May 2022

ER -

IVVES (Industrial-Grade Verification and Validation of Evolving Systems)

Abstract

Conference

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