Grant: Alliance Grants
University of Ontario Institute of Technology
$30,000.00
Test case prioritization using machine learning for large-scale continuous integration environment
FY 2022-23
Summary
Test prioritization and selection is of great importance in test process optimization to make efficient use of testing resources, especially when it comes to regression testing for different code submissions in the continuous integration of DevOps practice. Appropriate knowledge processing and learning techniques to deal with numerous and versatile sources of information can be used to address efficiently the peculiarities of each code submissions as well as software baselines and evaluate their quality characteristics. The cognitive test system in IBM has a long term goal to look at how to use machine learning and AI techniques to streamline and intelligently adapt testing environment to align with the changes and risks being introduced with each code change. A number of key contributions in this research will include: 1) developing a strategy that can automatically select the test suites that could be impacted by a change and identify the most likely ones to fail based on a given change, (2) modeling the size, pervasiveness and scope of a change and verify that by determining an optimal set of test suites to execute, and (3) using machine learning to model a developers skill in changing a given area and adjust test strategy accordingly.
Program
Alliance Grants
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
576129-2022
Name nom
Azim, Akramul A
Department département
Electrical, Computer and Software Engineering
Organization id
19222
Institution établissement
University of Ontario Institute of Technology
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2022
Competition year année de concours
2022
Award amount
30000
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
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
2720
Research subject group en
Information technology
Research subject group fr
Technologie de l'information
Research subject en
Computer systems software
Research subject fr
Logiciel de systèmes informatiques
Installment
inst:1
Partie
1
Nb partie
1
Application title
Test case prioritization using machine learning for large-scale continuous integration environment
Application summary
Test prioritization and selection is of great importance in test process optimization to make efficient use of testing resources, especially when it comes to regression testing for different code submissions in the continuous integration of DevOps practice. Appropriate knowledge processing and learning techniques to deal with numerous and versatile sources of information can be used to address efficiently the peculiarities of each code submissions as well as software baselines and evaluate their quality characteristics. The cognitive test system in IBM has a long term goal to look at how to use machine learning and AI techniques to streamline and intelligently adapt testing environment to align with the changes and risks being introduced with each code change. A number of key contributions in this research will include: 1) developing a strategy that can automatically select the test suites that could be impacted by a change and identify the most likely ones to fail based on a given change, (2) modeling the size, pervasiveness and scope of a change and verify that by determining an optimal set of test suites to execute, and (3) using machine learning to model a developers skill in changing a given area and adjust test strategy accordingly.
