Sophie Corallo

Sven Peldszus, Frederik Reiche, Kevin Hermann et al.

When assessing the potential impact of code-level vulnerabilities, e.g., discovered by automated analyzers, it is essential to consider them in the context of the system's security design. However, this is a challenging task due to the abstraction gap between security design, often specified using security DSLs, and implementation. As we will show, even security experts lack a complete understanding of this relationship. Intrigued by this gap (and the general disconnect between secure design and secure implementation) we present a study of 66 design-level security DSLs and 559 security checks from 36 code-level analyzers. We identify what concepts are common to both and capture them in the SecLan model, which has been validated by 22 security experts. Based on this, we investigate the relationship between DSLs and analyzers quantitatively and explore it qualitatively together with 9 security experts. We learn that there are few commonalities between design-level and implementation-level security; security checks are often described by overly general weaknesses, resulting in many non-obvious potential relationships between security DSLs and analyzers; and even security experts are overwhelmed by this complexity. We provide an empirical basis that helps practitioners and researchers better understand the gap and serves as a first step toward bridging it.

Sophie Corallo, Debora Grupp, Dominik Fuchß et al.

End-to-end security verification, from requirements through architecture to code, requires datasets that span all three artifact types with fine-grained security labels. No existing dataset provides this combination. We present the EVerest dataset, a multi-artifact resource based on EVerest, an industry-driven open-source software stack for electric vehicle charging stations. The dataset includes 84 manually elicited security requirements annotated with security objectives, 1,445 fine-grained security elements (components, entities, data, data flows, states, etc.), acceptance windows, coreferences, and architectural trace links, as well as the EVerest software architecture model, source code, and natural language documentation. It enables research on security requirements classification, named entity recognition, architectural trace linking, and design-time or code-level security verification. During dataset creation, a real security weakness (CWE-1295) was identified, disclosed to the project maintainers, and subsequently fixed. The dataset is publicly available. A short video is available at https://youtu.be/pnn1uqpomvQ.

Jan Keim, Dominik Fuchß, Sophie Corallo et al.

Context and Problem. Software development produces interrelated artifacts like software architecture documentation (SAD), software architecture models (SAMs), and source code, whose relationships are essential for maintenance and consistency checking. However, automatically recovering links between these artifacts (traceability link recovery (TLR)) remains difficult to deploy in practice. Method and Aim. We present an accessible tool landscape for ARDoCo's TLR approaches: the ARDoCo REST API exposes four TLR pipelines (SAD-SAM, SAM-Code, SAD-Code, and SAD-SAM-Code) via HTTP endpoints with asynchronous execution and caching; TraceView is a browser-based frontend with a guided wizard and interactive multi-panel exploration of recovered links and inconsistencies; and TraceViz, which is a VS Code extension that overlays trace links directly onto documentation in the IDE. Results and Conclusion. All three components are publicly deployed and usable. A preliminary study for TraceViz's in-IDE visualization confirmed that it improves developer comprehension during software understanding tasks. The tool landscape makes state-of-the-art TLR accessible to architects, developers, and tool integrators. Video. We provide a screencast of our ARDoCo Tool Landscape and how it is used here: https://youtu.be/IOTEPZQ3tVs