TY - GEN
T1 - Poster
T2 - 13th IEEE International Conference on Software Testing, Verification and Validation, ICST 2020
AU - Huang, Rubing
AU - Cui, Chenhui
AU - Sun, Weifeng
AU - Towey, Dave
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10
Y1 - 2020/10
N2 - Adaptive random testing (ART) aims at enhancing the testing effectiveness of random testing (RT) by more evenly spreading test cases over the input domain. Many ART methods have been proposed, based on various, different notions. For example, distance-based ART (DART) makes use of the concept of distance to implement ART, attempting to generate new test cases that are far away from previously executed ones. The Euclidean distance has been a popular choice of distance metric, used in DART to evaluate the differences between test cases. However, is the Euclidean distance the most suitable choice for DART? To answer this question, we conducted a series of simulations to investigate the impact that the Euclidean distance, and its many variations, has on the testing effectiveness of DART. The results show that when the dimensionality of the input domain is low, the Euclidean distance may indeed be a good choice. However, when the dimensionality is high, it appears to be less suitable.
AB - Adaptive random testing (ART) aims at enhancing the testing effectiveness of random testing (RT) by more evenly spreading test cases over the input domain. Many ART methods have been proposed, based on various, different notions. For example, distance-based ART (DART) makes use of the concept of distance to implement ART, attempting to generate new test cases that are far away from previously executed ones. The Euclidean distance has been a popular choice of distance metric, used in DART to evaluate the differences between test cases. However, is the Euclidean distance the most suitable choice for DART? To answer this question, we conducted a series of simulations to investigate the impact that the Euclidean distance, and its many variations, has on the testing effectiveness of DART. The results show that when the dimensionality of the input domain is low, the Euclidean distance may indeed be a good choice. However, when the dimensionality is high, it appears to be less suitable.
KW - Euclidean distance
KW - adaptive random testing
KW - dissimilarity metrics
UR - http://www.scopus.com/inward/record.url?scp=85091599541&partnerID=8YFLogxK
U2 - 10.1109/ICST46399.2020.00049
DO - 10.1109/ICST46399.2020.00049
M3 - Conference contribution
AN - SCOPUS:85091599541
T3 - Proceedings - 2020 IEEE 13th International Conference on Software Testing, Verification and Validation, ICST 2020
SP - 406
EP - 409
BT - Proceedings - 2020 IEEE 13th International Conference on Software Testing, Verification and Validation, ICST 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 23 March 2020 through 27 March 2020
ER -