technical consulting

This work addresses the challenges of low quality and poor transparency in build-or-buy decisions within enterprise software development, which often stem from reliance on unstructured experiential knowledge. To overcome these limitations—particularly in cold-start scenarios lacking historical data—the authors propose a structured approach that integrates a decision-factor ontology, rule-based reasoning, and reference-class matching. This method enables transparent, auditable evaluation of alternatives and represents the first application of combined ontology modeling and rule reasoning to build-or-buy decision-making. By revealing critical decision thresholds and supporting traceability, the approach enhances the rationality, transparency, and auditability of choices. Its practical efficacy is demonstrated through a lightweight tool validated in a financial industry case study, showing significant improvements in decision quality.

Current what-if analysis lacks a unified conceptual framework, leading to terminological inconsistency across domains, structural ambiguity, and divergent interpretations. To address this, we conduct a systematic review of 141 papers in visual analytics and human-computer interaction, proposing Praxa—the first integrative framework that unifies scenario modeling, sensitivity analysis, and counterfactual analysis under a coherent paradigm. Praxa formally defines the underlying motivations, core components (hypothesis generation, intervention modeling, outcome evaluation), and a taxonomy of analytical types. It establishes a standardized terminology and structured model, exposing critical challenges including interpretability, causal modeling fidelity, and alignment with user intent. By clarifying conceptual boundaries and operational relationships among methods, Praxa significantly enhances cross-domain conceptual consistency and application clarity. The framework provides a rigorous foundation for theoretical advancement and the design of next-generation interactive analytical tools.

Existing software architecture frameworks inadequately model machine learning (ML) systems, as they overlook the needs of emerging stakeholders—such as data scientists and data engineers—and lack expressive support for ML-specific characteristics, including component uncertainty, heterogeneity, and collaborative behavior. Method: Through an empirical study involving interviews and surveys with 61 domain experts from 25 organizations across 10 countries, we systematically identified ML-relevant stakeholders and their concerns for the first time. Contribution/Results: We propose novel, ML-adapted architectural viewpoints and views, extending traditional frameworks to enable unified modeling of both ML and non-ML components. This yields the *ML-Enhanced Systems Architecture Framework Extension Guide*, which has been preliminarily adopted in industry for intelligent system architecture governance. Our work bridges a critical theoretical and practical gap in stakeholder modeling and viewpoint systematization for ML system architecture design.

Safety requirements generated via STPA lack structured management and dynamic prioritization mechanisms. Method: This paper proposes an extensible closed-loop framework that integrates outputs across all STPA phases with multi-expert scoring, employs Monte Carlo simulation to quantify uncertainty and mitigate subjective bias, and enables robust requirement prioritization. Automated toolchain integration and a visual traceability matrix support end-to-end lifecycle tracking and decision-making—from conceptual design through high-level development. Contribution/Results: The framework is empirically validated in an eVTOL operations case study and has been formally adopted into the UK aviation regulatory document CAP3141. It significantly enhances efficiency, traceability, and regulatory compliance in safety requirement identification for emerging aviation systems.

Legacy systems written in COBOL, PL/I, or Assembly—common in banking and telecommunications—are often undocumented and lack original developers, hindering comprehension and modernization. Method: This paper proposes a multi-language, cross-platform, customizable framework for constructing software knowledge graphs and interactively defining architectural boundaries. It integrates static code analysis, data schema parsing, and custom ontology modeling to enable expert-guided, incremental analysis of source code and data architecture, automatically identifying business- and data-driven logical boundaries and visualizing cross-boundary dependencies. Contribution/Results: The framework introduces the first knowledge-graph-driven approach for progressive modernization path planning and impact analysis. Evaluated on two real-world industrial systems, it significantly improves system understanding efficiency and enhances the accuracy of modernization strategy design.

In organizations lacking a formal architect role, software architecture decisions are frequently made by practitioners without such titles. This study employs a mixed-methods approach, combining a survey of 54 practitioners with in-depth interviews of 7 participants, to investigate who actually makes architectural decisions and under what contextual conditions. The findings reveal that informal architects are extensively involved in critical architectural choices, while formally designated architect roles are primarily deemed necessary in large enterprises or complex teams. These results challenge the prevailing research paradigm that centers on formal architects, instead illuminating how architectural responsibilities are distributed across real-world development environments and highlighting their strong dependence on organizational context.

In multi-stakeholder platforms, software architecture decisions often implicitly entrench conflicting requirements without systematic support for mapping governance principles to technical design. This work proposes the first governance-architecture alignment framework, explicitly linking five core governance principles to the space of architectural decisions, thereby rendering implicit governance stances identifiable and contestable. The framework also exposes how default technical choices can obscure underlying value commitments. Feasibility is preliminarily demonstrated through a constructive case study of a pig-farming knowledge platform in Rwanda. Future work will employ pre- and post-intervention user judgment studies to evaluate the framework’s impact on actual governance outcomes.

This work addresses the longstanding reliance on manual, time-consuming, and subjectivity-prone approaches in software architecture quality assessment, particularly in scenarios involving trade-offs among multiple quality attributes. To overcome these limitations, the study introduces generative large language models (LLMs) into this domain for the first time, leveraging Microsoft Copilot in conjunction with the Architecture Tradeoff Analysis Method (ATAM) and quality attribute scenario techniques. The proposed approach enables automated identification of architectural risks, analysis of sensitivity points, and generation of trade-off recommendations. Experimental results demonstrate that, in most cases, the method achieves higher accuracy and efficiency compared to human-led reviews, substantially reducing evaluation costs while improving consistency across assessments.

This study addresses the challenge faced by production system engineers in automatically verifying production line layouts due to limited knowledge of PDDL and planning theory. To bridge this gap, the authors propose a novel approach based on an Asset Administration Shell (AAS) capability model that natively generates complete PDDL planning problems directly from domain-level descriptions, eliminating the need for PDDL-specific submodels. The method integrates four Industry 4.0 standards—VDI 3682, IEC 61360-1, IDTA 02011, and IDTA 02016—to construct the AAS and employs an extraction algorithm to automatically translate multi-AAS architectures into PDDL domains. In a laboratory case study, the approach enabled engineers to systematically compare four layout variants by modifying only the AAS model, significantly lowering the barrier to adopting automated planning in industrial settings.

本文提出了一种基于集合论的操作方法和AAS适用性模型，以解决不同AAS实例在结构、内容及完整性上的差异问题，从而提高其在特定应用场景中的可比性和适用性。