TY - GEN
T1 - Preparing Software Quality Assurance Professionals
T2 - 2019 IEEE International Conference on Engineering, Technology and Education, TALE 2019
AU - Yang, Sen
AU - Towey, Dave
AU - Zhou, Zhi Quan
AU - Chen, T. Y.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - Computer science (CS) subjects have been rapidly growing in popularity, and demand for CS education and training has put increasing pressure on teaching resources in higher education (HE) and elsewhere. HE in the People's Republic of China (PRC) has also been developing, with one product of this evolution being Sino-foreign HE institutions (SfHEIs). Much of the popularity growth for CS can be linked to the growth of CS-based technology and innovation, especially in the form of Artificial Intelligence (AI) and Machine Learning (ML). AI/ML-based innovation has been forecast to offer increases in quality of life for consumers. However, AI/ML systems face a challenge for software quality assurance (SQA): They are so-called 'untestable systems' - identifying the correctness of AI/ML system outputs or behaviour may not be feasible. Preparing SQA professionals to be able to ensure AI/ML SQA will require innovative and creative education and training. An SQA approach called metamorphic testing (MT) has a proven track record of alleviating the oracle problem, and has great potential as a testing methodology for AI/ML systems. Metamorphic exploration (ME) is a new addition to the MT literature, and involves developing the user's understanding of the system under study. This paper reports on experiences at an SfHEI of using ME and MT to test an AI/ML system.
AB - Computer science (CS) subjects have been rapidly growing in popularity, and demand for CS education and training has put increasing pressure on teaching resources in higher education (HE) and elsewhere. HE in the People's Republic of China (PRC) has also been developing, with one product of this evolution being Sino-foreign HE institutions (SfHEIs). Much of the popularity growth for CS can be linked to the growth of CS-based technology and innovation, especially in the form of Artificial Intelligence (AI) and Machine Learning (ML). AI/ML-based innovation has been forecast to offer increases in quality of life for consumers. However, AI/ML systems face a challenge for software quality assurance (SQA): They are so-called 'untestable systems' - identifying the correctness of AI/ML system outputs or behaviour may not be feasible. Preparing SQA professionals to be able to ensure AI/ML SQA will require innovative and creative education and training. An SQA approach called metamorphic testing (MT) has a proven track record of alleviating the oracle problem, and has great potential as a testing methodology for AI/ML systems. Metamorphic exploration (ME) is a new addition to the MT literature, and involves developing the user's understanding of the system under study. This paper reports on experiences at an SfHEI of using ME and MT to test an AI/ML system.
KW - Sino-foreign higher education
KW - artificial antelligence (AI)
KW - computer science
KW - machine learning (ML)
KW - metamorphic exploration (ME)
KW - metamorphic testing (MT)
KW - oracle problem
KW - software quality assurance (SQA)
UR - http://www.scopus.com/inward/record.url?scp=85094171913&partnerID=8YFLogxK
U2 - 10.1109/TALE48000.2019.9225946
DO - 10.1109/TALE48000.2019.9225946
M3 - Conference contribution
AN - SCOPUS:85094171913
T3 - TALE 2019 - 2019 IEEE International Conference on Engineering, Technology and Education
BT - TALE 2019 - 2019 IEEE International Conference on Engineering, Technology and Education
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 8 April 2019 through 11 April 2019
ER -
