Exploiting traceability uncertainty between software architectural models and extra-functional results

Deriving extra-functional properties (e.g., performance, security, reliability) from software architectural models is the cornerstone of software development as it supports the designers with quantitative predictions of system qualities. However, the problem of interpreting results from quantitative analysis of extra-functional properties is still challenging because it is hard to understand how the analysis results (e.g., response time, data confidentiality, mean time to failure) trace back to the architectural model elements (i.e., software components, interactions among components, deployment nodes). The goal of this paper is to automate the traceability between software architectural models and extrafunctional results, such as performance and security, by investigating the uncertainty while bridging these two domains. Our approach makes use of extra-functional patterns and antipatterns, such as performance antipatterns and security patterns, to deduce the logical consequences between the architectural elements and analysis results and automatically build a graph of traces, thus to identify the most critical causes of extra-functional flaws. We developed a tool that jointly considers SOftware and Extra-Functional concepts (SoEfTraceAnalyzer), and it automatically builds model-to-results traceability links. This paper demonstrates the effectiveness of our automated and tool supported approach on three case studies, i.e., two academic research projects and one industrial system.