RT Journal Article
T1 ExTrA: Explaining architectural design tradeoff spaces via dimensionality reduction.
A1 Cámara-Moreno, Javier
A1 Wohlrab, Rebekka
A1 Garlan, David
A1 Schmerl, Bradley
K1 Diseño arquitectónico
K1 Ingeniería del software
AB In software design, guaranteeing the correctness of run-time system behavior while achieving an acceptable balance among multiple quality attributes remains a challenging problem. Moreover, providing guarantees about the satisfaction of those requirements when systems are subject to uncertain environments is even more challenging. While recent developments in architectural analysis techniques can assist architects in exploring the satisfaction of quantitative guarantees across the design space, existing approaches are still limited because they do not explicitly link design decisions to satisfaction of quality requirements. Furthermore, the amount of information they yield can be overwhelming to a human designer, making it difficult to see the forest for the trees. In this paper we present ExTrA (Explaining Tradeoffs of software Architecture design spaces), an approach to analyzing architectural design spaces that addresses these limitations and provides a basis for explaining design tradeoffs. Our approach employs dimensionality reduction techniques employed in machine learning pipelines like Principal Component Analysis (PCA) and Decision Tree Learning (DTL) to enable architects to understand how design decisions contribute to the satisfaction of extra-functional properties across the design space. Our results show feasibility of the approach in two case studies and evidence that combining complementary techniques like PCA and DTL is a viable approach to facilitate comprehension of tradeoffs in poorly-understood design spaces.
PB Elsevier
YR 2022
FD 2022-12-19
LK https://hdl.handle.net/10630/26398
UL https://hdl.handle.net/10630/26398
LA eng
NO Cámara, J., Wohlrab, R., Garlan, D., & Schmerl, B. (2023). ExTrA: Explaining architectural design tradeoff spaces via dimensionality reduction. Journal of Systems and Software, 198, 111578.
NO This work was partially supported by the Spanish Government (FEDER/Ministerio de Ciencia e Innovación – Agencia Estatal de Investigación) under project COSCA (PGC2018-094905-B-I00), by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation, Sweden, by award N00014172899 from the Office of Naval Research, United States of America, and the NSA, United States of America under Award No. H9823018D0008. Any views, opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views the funding agencies.   //    Funding for open access charge: Universidad de Málaga / CBUA
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026