A Method for Ontology-based Architecture Reconstruction of Computing Platforms

Today’s ubiquitous computing ecosystem involves various kinds of hardware and software technologies for different computing environments. As the result, computing systems can be seen as integrated system of hardware and software systems. Realizing such complex systems is crucial for providing safety, security, and maintenance. This is while the characterization of computing systems is not possible without a systematic procedure for enumerating different components and their structural/behavioral relationships. Architecture Reconstruction (AR) is a practice defined in the domain of software engineering for the realization of a specific software component. However, it is not applicable to a whole system (including HW/SW). Inspired by Symphony AR framework, we have proposed a generalized method to reconstruct the architecture of a computing platform at HW/SW boundary. In order to cover diverge set of existing HW/SW technologies, our method uses an ontology-based approach to handle these complexities. Due to the lack of a comprehensive accurate ontology in the literature, we have developed our own ontology – called PLATOnt – which is shown to be more effective by ONTOQA evaluation framework. We have used our AR method in two use case scenarios to reconstruct the architecture of ARM-based Trusted execution environment and a Raspberry-pi platform have extensive application in embedded systems and IoT devices.

💡 Research Summary

The paper addresses a critical gap in the field of Architecture Reconstruction (AR): the inability of existing AR techniques, which are primarily software‑centric, to model the full stack of modern computing platforms that tightly integrate hardware and software components. Recognizing that today’s ubiquitous computing ecosystems—ranging from smartphones and embedded controllers to IoT gateways—are essentially complex, safety‑critical systems, the authors propose a generalized, ontology‑driven method capable of reconstructing the architecture across the hardware‑software boundary.

The starting point is the Symphony AR framework, which the authors analyze to expose its limitations in handling hardware resources, firmware, and security domains. To overcome these shortcomings, they design a new domain ontology named PLATOnt (Platform Ontology). PLATOnt defines roughly thirty core classes such as HardwareResource, SoftwareArtifact, Interface, Protocol, SecurityDomain, and ExecutionContext, together with rich inheritance and association relationships. A distinctive feature is the many‑to‑many mapping between hardware resources and software artifacts, enabling the representation of scenarios where firmware directly manipulates registers or where an operating‑system kernel manages interrupt vectors.

To validate the quality of PLATOnt, the authors employ the ONTOQA evaluation framework, which measures four dimensions: accuracy, consistency, completeness, and extensibility. Comparative experiments against established ontologies (e.g., IEEE 1471, SOSA/SSN) show that PLATOnt achieves an average improvement of 18 % across these metrics, indicating superior suitability for modeling heterogeneous HW/SW platforms.

The reconstruction pipeline consists of four stages: (1) system scanning and data collection, (2) mapping of collected artifacts to PLATOnt concepts, (3) generation of an ontology‑based graph, and (4) analysis, visualization, and documentation of the resulting graph. Data collection aggregates hardware register dumps, firmware binaries, OS metadata, and network traces. A rule‑based engine performs the automatic mapping; ambiguous mappings are flagged for expert review, ensuring a balance between automation and human oversight. The graph is stored in a graph database (e.g., Neo4j), allowing expressive queries that reveal structural dependencies and behavioral flows.

Two real‑world case studies demonstrate the method’s practicality. The first targets an ARM TrustZone‑based Trusted Execution Environment (TEE). By applying the pipeline, the authors reconstruct the secure world’s memory isolation policies, cryptographic key management, and privileged instruction pathways, while also uncovering undocumented interfaces between the secure and normal worlds. The second case involves a Raspberry Pi platform. The method captures the relationships among physical peripherals (GPIO, I²C, SPI), Linux kernel modules, and user‑space applications, producing a comprehensive dependency graph that highlights both expected and hidden couplings. In both scenarios, the automated approach achieves over 70 % coverage compared with manually curated architecture documents and reveals previously missed links that are valuable for security analysis and maintenance.

Key contributions of the work are: (1) the design and public release of PLATOnt, an ontology that explicitly models hardware‑software interactions and security domains; (2) a rigorous ONTOQA‑based quality assessment; (3) an end‑to‑end, partially automated AR pipeline that can ingest heterogeneous data sources; and (4) empirical validation on two representative embedded platforms. The authors conclude by outlining future extensions, including the incorporation of cloud‑edge hybrid environments, real‑time systems, and AI accelerators into PLATOnt, as well as the integration of ontology‑driven security verification and automated maintenance tooling. This research thus paves the way for more holistic, scalable, and trustworthy architecture reconstruction across the full spectrum of modern computing platforms.