Grant: Alliance Grants
Centre de recherche informatique de Montréal
$121,000.00
Industrial-grade Verification & Validation of Evolving Systems - Canadian cluster
FY 2021-22
Summary
Evolving systems (ESs) form a new class of systems that can rapidly change their behavior, due to fast iteration cycles in development and/or to their ability to self-adapt and learn; they can include Machine Learning (ML)-enabled components. By 2025 the market for artificial intelligence related products in Canada is expected to reach $127 billion. The footprint of ML-based (critical) applications is dramatically increasing in Canada and worldwide. Critical domains include but are not limited to Aerospace, Cybersecurity, Automotive & Transportation, Banking & Finance and Business and data analytics. ESs do not follow the modelling, design and implementation standards developed for traditional systems: the behavior of ES is less transparent than for most of traditional systems; the development of ESs is agile and often driven by data or user-evolving expectations. Formal specifications for ESs can be missing and requirements expressed with data or unstructured text are often available. Industrial-grade testing and verification (T&V) approaches for a comprehensive and thorough quality assurance of ESs are needed by the industry and for our safety. CRIM, a Canadian research organisation, and three Canadian industrial organisations teamed up over 20 organisations from four European countries to fulfil the industrial needs within the context of the project IVVES (Industrial-graded Verification and Validation of Evolving Systems). IVVES has been approved by ITEA that is a cluster of EUREKA. The research conducted for 3 years within IVVES-CC will propose systematic approaches for robust and comprehensive, industrial-grade T&V of ESs. It develops (1) T&V approaches dedicated to ML-enabled applications, (2) AI and data-driven T&V approaches dedicated to ESs to cover areas that currently cannot be covered by state-of-the-art specification-based testing and (3) smart engineering approaches that use data analytics to establish efficient and high-quality engineering processes for ESs. The approaches will be evaluated on industrial use cases; HQP and industrial partners will be trained to master the developed approaches for a better roll-out in the industry. The project is partially supported by CRIAQ.
Program
Alliance Grants
Location
Quebec, Canada
Recipient Type
Not-for-profit organization or charity
Type
Grant
Source
NSERC's Awards Data
As published
Application id
549118-2019
Name nom
Petrenko, Alexandre
Department département
Analyse de systèmes distribués
Organization id
71
Institution établissement
Centre de recherche informatique de Montréal
Province en
Québec
Province fr
Québec
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2021
Competition year année de concours
2019
Award amount
121000
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
802
Area of application group en
Information and communication services
Area of application group fr
Services d'information et de télécommunications
Area of application en
Information systems and technology
Area of application fr
Systèmes d'information et technologie de l'information
Research subject code
2706
Research subject group en
Information technology
Research subject group fr
Technologie de l'information
Research subject en
Software engineering
Research subject fr
Génie logiciel
Installment
inst:2
Partie
1
Nb partie
3
Application title
Industrial-grade Verification & Validation of Evolving Systems - Canadian cluster
Keyword
NSERC
Application summary
Evolving systems (ESs) form a new class of systems that can rapidly change their behavior, due to fast iteration cycles in development and/or to their ability to self-adapt and learn; they can include Machine Learning (ML)-enabled components. By 2025 the market for artificial intelligence related products in Canada is expected to reach $127 billion. The footprint of ML-based (critical) applications is dramatically increasing in Canada and worldwide. Critical domains include but are not limited to Aerospace, Cybersecurity, Automotive & Transportation, Banking & Finance and Business and data analytics. ESs do not follow the modelling, design and implementation standards developed for traditional systems: the behavior of ES is less transparent than for most of traditional systems; the development of ESs is agile and often driven by data or user-evolving expectations. Formal specifications for ESs can be missing and requirements expressed with data or unstructured text are often available. Industrial-grade testing and verification (T&V) approaches for a comprehensive and thorough quality assurance of ESs are needed by the industry and for our safety. CRIM, a Canadian research organisation, and three Canadian industrial organisations teamed up over 20 organisations from four European countries to fulfil the industrial needs within the context of the project IVVES (Industrial-graded Verification and Validation of Evolving Systems). IVVES has been approved by ITEA that is a cluster of EUREKA. The research conducted for 3 years within IVVES-CC will propose systematic approaches for robust and comprehensive, industrial-grade T&V of ESs. It develops (1) T&V approaches dedicated to ML-enabled applications, (2) AI and data-driven T&V approaches dedicated to ESs to cover areas that currently cannot be covered by state-of-the-art specification-based testing and (3) smart engineering approaches that use data analytics to establish efficient and high-quality engineering processes for ESs. The approaches will be evaluated on industrial use cases; HQP and industrial partners will be trained to master the developed approaches for a better roll-out in the industry. The project is partially supported by CRIAQ.
