TY - GEN
T1 - Capturing Requirement Correlation in Adaptive Systems
AU - Anggraini, Ratih N.E.
AU - Martin, T. P.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/3/25
Y1 - 2019/3/25
N2 - An adaptive system is expected to modify its behavior to suit changes in environmental and system condition. Somehow, it causes problems during requirement specification (and in subsequent verification) since it is difficult to provide all possible adaptation needed during runtime. Thus, it may be necessary to temporarily ignore non-critical requirements to a certain point in order to maintain satisfaction, especially on critical and invariant requirements.One way to handle uncertainty in the adaptive system is by relaxing requirement and present the verification result using a graded (fuzzy) condition in a requirement satisfaction. Often, relaxing one requirement can affect the satisfaction of another related requirement. In this paper, we use linear regression to capture the relationship between two relaxed system requirements. Pearson and Spearman correlation coefficient is utilized to calculate correlation strength. To illustrate the approach, we consider a smart vacuum system problem.
AB - An adaptive system is expected to modify its behavior to suit changes in environmental and system condition. Somehow, it causes problems during requirement specification (and in subsequent verification) since it is difficult to provide all possible adaptation needed during runtime. Thus, it may be necessary to temporarily ignore non-critical requirements to a certain point in order to maintain satisfaction, especially on critical and invariant requirements.One way to handle uncertainty in the adaptive system is by relaxing requirement and present the verification result using a graded (fuzzy) condition in a requirement satisfaction. Often, relaxing one requirement can affect the satisfaction of another related requirement. In this paper, we use linear regression to capture the relationship between two relaxed system requirements. Pearson and Spearman correlation coefficient is utilized to calculate correlation strength. To illustrate the approach, we consider a smart vacuum system problem.
KW - Pearson correlation coefficient
KW - Spearman correlation coefficient
KW - adaptive system
KW - linear regression
KW - relax requirement
UR - https://www.scopus.com/pages/publications/85064392000
U2 - 10.1109/ROBIONETICS.2018.8674673
DO - 10.1109/ROBIONETICS.2018.8674673
M3 - Conference contribution
AN - SCOPUS:85064392000
T3 - Proceedings of the 2018 International Conference on Robotics, Biomimetics, and Intelligent Computational Systems, Robionetics 2018
SP - 6
EP - 11
BT - Proceedings of the 2018 International Conference on Robotics, Biomimetics, and Intelligent Computational Systems, Robionetics 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 International Conference on Robotics, Biomimetics, and Intelligent Computational Systems, Robionetics 2018
Y2 - 8 August 2018 through 10 August 2018
ER -