Mitigation strategies for confidentiality violations in software architecture using ranked feature importance

A quality attribute like confidentiality is critical to trustworthy software but unfortunately, very challenging to
ensure. This is because modern software systems are complex and interconnected. Architecture-based confiden-
tiality analysis enables the early detection of violations, helping to mitigate risks before deployment. However,
uncertainty in software systems and their environments complicates precise and comprehensive architectural
analysis. Additionally, the complexity of software models and the exponential growth of uncertainty scenarios
pose significant challenges for automated mitigation, often leaving software architects to resolve confidentiality
violations manually, a process that is both time-intensive and error-prone.
In this paper, we extend our machine-learning-based approach to mitigate confidentiality violations. Specif-
ically, we introduce a novel mitigation strategy inspired by TCP Congestion Control, as well as a strategy that
capitalizes on clustering techniques to dynamically adjust batch sizes. Our evaluation on three real-world soft-
ware architectures demonstrates that our extended approach can mitigate confidentiality violations while out-
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performing the state-of-the-art. Whereas previously the upper limit was 60 times runtime reduction, now we
achieve 2298 times reduction, with the median being an elevenfold reduction. Our statistical analysis confirms
that the added TCP-inspired strategy is significantly cheaper than the state-of-the-art baseline (Friedman test
𝑝 = .025 and Nemenyi post hoc test 𝑝 = .039), while also having a strong practical impact (Kendall’s W = 0.721).
This extended work deepens our understanding of the nature of uncertainty and also of the techniques optimally
suited to mitigating the violations caused by uncertainties. It takes us one step closer to designing trustworthier
systems.