Abstract
The adoption of machine learning (ML) components in software systems raises new engineering challenges. In particular, the inherent uncertainty regarding functional suitability and the operation environment makes architecture evaluation and trade-off analysis difficult. We propose a software architecture evaluation method called Modeling Uncertainty During Design (MUDD) that explicitly models the uncertainty associated to ML components and evaluates how it propagates through a system. The method supports reasoning over how architectural patterns can mitigate uncertainty and enables comparison of different architectures focused on the interplay between ML and classical software components. While our approach is domain-agnostic and suitable for any system where uncertainty plays a central role, we demonstrate our approach using as example a perception system for autonomous driving.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Behere, S., Törngren, M.: A functional reference architecture for autonomous driving. Inf. Softw. Technol. 73, 136–150 (2016)
Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: IEEE CVPR, pp. 3213–3223 (2016)
Esfahani, N., Malek, S.: Uncertainty in self-adaptive software systems. In: de Lemos, R., Giese, H., Müller, H.A., Shaw, M. (eds.) Software Engineering for Self-Adaptive Systems II. LNCS, vol. 7475, pp. 214–238. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-35813-5_9
Esfahani, N., Malek, S., Razavi, K.: GuideArch: guiding the exploration of architectural solution space under uncertainty. In: ICSE, pp. 43–52. IEEE (2013)
Etxeberria, L., Trubiani, C., Cortellessa, V., Sagardui, G.: Performance-based selection of software and hardware features under parameter uncertainty. In: International Conference on Quality of Software Architectures, pp. 23–32 (2014)
Mahdavi-Hezavehi, S., Avgeriou, P., Weyns, D.: A classification framework of uncertainty in architecture-based self-adaptive systems with multiple quality requirements. In: Managing Trade-Offs in Adaptable Software Architectures, pp. 45–77. Elsevier (2017)
Meedeniya, I., Aleti, A., Grunske, L.: Architecture-driven reliability optimization with uncertain model parameters. JSS 85(10), 2340–2355 (2012)
Perez-Palacin, D., Mirandola, R.: Uncertainties in the modeling of self-adaptive systems: a taxonomy and an example of availability evaluation. In: ACM/SPEC International Conference on Performance Engineering, pp. 3–14 (2014)
Serban, A., Poll, E., Visser, J.: Adversarial examples on object recognition: a comprehensive survey. ACM Comput. Surv. (CSUR) 53, 1–38 (2020)
Serban, A., Poll, E., Visser, J.: A standard driven software architecture for fully autonomous vehicles. In: JASE, pp. 20–33. Atlantis Press (2020)
Serban, A.C.: Designing safety critical software systems to manage inherent uncertainty. In: IEEE ICSA-C, pp. 246–249. IEEE (2019)
Teichmann, M., Weber, M., Zoellner, M., Cipolla, R., Urtasun, R.: MultiNet: real-time joint semantic reasoning for autonomous driving. In: Intelligent Vehicles, pp. 1013–1020. IEEE (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Serban, A., Poll, E., Visser, J. (2020). Towards Using Probabilistic Models to Design Software Systems with Inherent Uncertainty. In: Jansen, A., Malavolta, I., Muccini, H., Ozkaya, I., Zimmermann, O. (eds) Software Architecture. ECSA 2020. Lecture Notes in Computer Science(), vol 12292. Springer, Cham. https://doi.org/10.1007/978-3-030-58923-3_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-58923-3_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-58922-6
Online ISBN: 978-3-030-58923-3
eBook Packages: Computer ScienceComputer Science (R0)