TY - GEN
T1 - Done is better than perfect
T2 - 30th IEEE International Requirements Engineering Conference, RE 2022
AU - Li, Jialong
AU - Tei, Kenji
N1 - Funding Information:
ACKNOWLEDGMENT The research was partially supported by JSPS KAKENHI and JSPS Research Fellowships for Young Scientists. The authors would like to thank Mr. Yichen Ding for comments and corrections to this paper.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In the studies of self-adaptive systems (SAS), requirement relaxation is a widely discussed approach for managing the system's requirements when dealing with the runtime environment changes (e.g., ignoring low-priority requirements to guarantee high-priority requirements). Guaranteeable requirement analysis (GRA) is recently proposed to determine the relaxation by checking the feasibility of all requirement combinations, enabling the SAS to realize the relaxation autonomously. However, a critical problem of GRA is the trade-off between analysis/relaxation precision and computation time at different granularity levels of requirements. Specifically, the analysis may not be precise enough if the requirements are coarse-grained (i.e., high granularity level), while the analysis may take a too long time if the requirements are fine-grained (i.e., low granularity level). This paper proposed a method, namely iterative adaptation via multi-grained requirement relaxation, to achieve the advantages of high precision and short computation time. Specifically, the SAS first deploys a rapid (but imprecise) relaxation using high granularity-level requirements. It then repeatedly iterates to a preciser (but slower) relaxation with a progressive decrease in the granularity level. An experiment based on the warehouse robot system demonstrates the validity of our proposal.
AB - In the studies of self-adaptive systems (SAS), requirement relaxation is a widely discussed approach for managing the system's requirements when dealing with the runtime environment changes (e.g., ignoring low-priority requirements to guarantee high-priority requirements). Guaranteeable requirement analysis (GRA) is recently proposed to determine the relaxation by checking the feasibility of all requirement combinations, enabling the SAS to realize the relaxation autonomously. However, a critical problem of GRA is the trade-off between analysis/relaxation precision and computation time at different granularity levels of requirements. Specifically, the analysis may not be precise enough if the requirements are coarse-grained (i.e., high granularity level), while the analysis may take a too long time if the requirements are fine-grained (i.e., low granularity level). This paper proposed a method, namely iterative adaptation via multi-grained requirement relaxation, to achieve the advantages of high precision and short computation time. Specifically, the SAS first deploys a rapid (but imprecise) relaxation using high granularity-level requirements. It then repeatedly iterates to a preciser (but slower) relaxation with a progressive decrease in the granularity level. An experiment based on the warehouse robot system demonstrates the validity of our proposal.
KW - discrete controller synthesis
KW - RE@runtime
KW - requirement granularity
KW - requirement relaxation
KW - self-adaptive system
UR - http://www.scopus.com/inward/record.url?scp=85141002914&partnerID=8YFLogxK
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U2 - 10.1109/RE54965.2022.00043
DO - 10.1109/RE54965.2022.00043
M3 - Conference contribution
AN - SCOPUS:85141002914
T3 - Proceedings of the IEEE International Conference on Requirements Engineering
SP - 288
EP - 294
BT - Proceedings - 30th IEEE International Requirements Engineering Conference, RE 2022
A2 - Knauss, Eric
A2 - Mussbacher, Gunter
A2 - Arora, Chetan
A2 - Bano, Muneera
A2 - Schneider, Jean-Guy
PB - IEEE Computer Society
Y2 - 15 August 2022 through 19 August 2022
ER -