eQual: Informing Early Design Decisions
by Arman Shahbazian, Suhrid Karthik, Yuriy Brun, Nenad Medvidovic
Abstract:

When designing a software system, architects make a series of design decisions that directly impact the system's quality. The number of available design alternatives grows rapidly with system size, creating an enormous space of intertwined design concerns that renders manual exploration impractical. We present eQual, a model-driven technique for simulation-based assessment of architectural designs. While it is not possible to guarantee optimal decisions so early in the design process, eQual improves decision quality. eQual is effective in practice because it (1) limits the amount of information the architects have to provide and (2) adapts optimization algorithms to effectively explore massive spaces of design alternatives. We empirically demonstrate that eQual yields designs whose quality is comparable to a set of systems' known optimal designs. A user study shows that, compared to the state-of-the-art, engineers using eQual produce statistically significantly higher-quality designs with a large effect size, are statistically significantly more confident in their designs, and find eQual easier to use.

Citation:
Arman Shahbazian, Suhrid Karthik, Yuriy Brun, and Nenad Medvidovic, eQual: Informing Early Design Decisions, in Proceedings of the the 27th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), 2020.
Bibtex:
@inproceedings{Shahbazian20fse,
  author = {Arman Shahbazian and Suhrid Karthik and Yuriy Brun and Nenad Medvidovic},
  title = {\href{http://people.cs.umass.edu/brun/pubs/pubs/Shahbazian20fse.pdf}{eQual: Informing Early Design Decisions}},
  booktitle = {Proceedings of the the 27th ACM Joint European Software
  Engineering Conference and Symposium on the Foundations of Software
  Engineering (ESEC/FSE)},
  venue = {ESEC/FSE},
  month = {November},
  year = {2020},
  date = {11--13},
  address = {Sacramento, CA, USA},
  doi = {10.1145/3368089.3409749},

  accept = {$\frac{101}{360} \approx 28\%$},
  
  note = {ACM artifact badges granted: 
  \href{https://www.acm.org/publications/policies/artifact-review-badging}{Artifact
  Available \raisebox{-.75ex}{\includegraphics[height=2.5ex]{ACMArtifactAvailable}}}. 
  \href{https://doi.org/10.1145/3368089.3409749}{DOI: 10.1145/3368089.3409749}},

  abstract = {<p>When designing a software system, architects make a series of design
  decisions that directly impact the system's quality. The number of available
  design alternatives grows rapidly with system size, creating an enormous
  space of intertwined design concerns that renders manual exploration
  impractical. We present eQual, a model-driven technique for simulation-based
  assessment of architectural designs. While it is not possible to guarantee
  optimal decisions so early in the design process, eQual improves decision
  quality. eQual is effective in practice because it (1) limits the amount of
  information the architects have to provide and (2) adapts optimization
  algorithms to effectively explore massive spaces of design alternatives. We
  empirically demonstrate that eQual yields designs whose quality is comparable
  to a set of systems' known optimal designs. A user study shows that, compared
  to the state-of-the-art, engineers using eQual produce statistically
  significantly higher-quality designs with a large effect size, are
  statistically significantly more confident in their designs, and find eQual
  easier to use.</p>},

}