Abstract
Various disciplines use models for different purposes. While engineering models, including software engineering models, are often developed to guide the construction of a non- existent system, scientific models, in contrast, are created to better understand a natural phenomenon (i.e., an already existing system). An engineering model may incorporate scientific models to build a system. Both engineering and scientific models have been used to support sustainability, but largely in a loosely-coupled fashion, independently developed and maintained from each other. Due to the inherent complex nature of sustainability that must balance trade-offs between social, environmental, and economic concerns, modeling challenges abound for both the scientific and engineering disciplines. This paper offers a vision that synergistically combines engineering and scientific models to enable broader engagement of society for addressing sustain- ability concerns, informed decision-making based on more- accessible scientific models and data, and automated feed- back to the engineering models to support dynamic adaptation of sustainability systems. To support this vision, we identify a number of research challenges to be addressed with particular emphasis on the socio-technical benefits of modeling.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 8th International Workshop on Modeling in Software Engineering, MiSE 2016 |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 62-66 |
| Number of pages | 5 |
| ISBN (Electronic) | 978-1-4503-4164-6 |
| DOIs | |
| Publication status | Published - 14 May 2016 |
| Event | 8th International Workshop on Modeling in Software Engineering, MiSE 2016 - Austin, United States Duration: 16 May 2016 → 17 May 2016 |
Conference
| Conference | 8th International Workshop on Modeling in Software Engineering, MiSE 2016 |
|---|---|
| Country | United States |
| City | Austin |
| Period | 16/05/16 → 17/05/16 |
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Keywords
- Computer Science
- Software Engineering
Cite this
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Modeling for sustainability. / Combemale, Benoit; Cheng, Betty H.C.; Moreira, Ana; Bruel, Jean Michel; Gray, Jeff.
Proceedings - 8th International Workshop on Modeling in Software Engineering, MiSE 2016. Association for Computing Machinery, Inc, 2016. p. 62-66.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Modeling for sustainability
AU - Combemale, Benoit
AU - Cheng, Betty H.C.
AU - Moreira, Ana
AU - Bruel, Jean Michel
AU - Gray, Jeff
PY - 2016/5/14
Y1 - 2016/5/14
N2 - Various disciplines use models for different purposes. While engineering models, including software engineering models, are often developed to guide the construction of a non- existent system, scientific models, in contrast, are created to better understand a natural phenomenon (i.e., an already existing system). An engineering model may incorporate scientific models to build a system. Both engineering and scientific models have been used to support sustainability, but largely in a loosely-coupled fashion, independently developed and maintained from each other. Due to the inherent complex nature of sustainability that must balance trade-offs between social, environmental, and economic concerns, modeling challenges abound for both the scientific and engineering disciplines. This paper offers a vision that synergistically combines engineering and scientific models to enable broader engagement of society for addressing sustain- ability concerns, informed decision-making based on more- accessible scientific models and data, and automated feed- back to the engineering models to support dynamic adaptation of sustainability systems. To support this vision, we identify a number of research challenges to be addressed with particular emphasis on the socio-technical benefits of modeling.
AB - Various disciplines use models for different purposes. While engineering models, including software engineering models, are often developed to guide the construction of a non- existent system, scientific models, in contrast, are created to better understand a natural phenomenon (i.e., an already existing system). An engineering model may incorporate scientific models to build a system. Both engineering and scientific models have been used to support sustainability, but largely in a loosely-coupled fashion, independently developed and maintained from each other. Due to the inherent complex nature of sustainability that must balance trade-offs between social, environmental, and economic concerns, modeling challenges abound for both the scientific and engineering disciplines. This paper offers a vision that synergistically combines engineering and scientific models to enable broader engagement of society for addressing sustain- ability concerns, informed decision-making based on more- accessible scientific models and data, and automated feed- back to the engineering models to support dynamic adaptation of sustainability systems. To support this vision, we identify a number of research challenges to be addressed with particular emphasis on the socio-technical benefits of modeling.
KW - Computer Science
KW - Software Engineering
UR - http://www.scopus.com/inward/record.url?scp=84973513196&partnerID=8YFLogxK
U2 - 10.1145/2896982.2896992
DO - 10.1145/2896982.2896992
M3 - Conference contribution
SP - 62
EP - 66
BT - Proceedings - 8th International Workshop on Modeling in Software Engineering, MiSE 2016
PB - Association for Computing Machinery, Inc
ER -