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 impossible 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. A user study shows that, compared to the prior 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},

  accept = {$\frac{101}{360} \approx 28\%$},

  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
  impossible 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. A user study shows that, compared to the prior 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>},

}