«Article» Engineering Mechatronic Product Lines using the Docs-as-Code Approach#

  • License: CC BY-SA-4.0

  • Copyright: Alex Mann-Wahrenberg (basejumpa) 2025

Engineering-Grade Documentation for Mechatronic Product Lines#

Building successful mechatronic product lines means navigating the intersection of software, electronics, and mechanical design — often under tight timelines and strict quality, safety, and regulatory requirements.

In such an environment, documentation is not an afterthought. It is an engineering discipline of its own. Specifications, interfaces, workflows, and handbooks must be:

  • Accurate and versioned,

  • Readable and reviewable,

  • Traceable and testable.

This article proposes a Docs-as-Code approach to documentation in Systems, Software, and Hardware Engineering — using engineering tools, engineering workflows, and engineering discipline. It introduces HermesBaby, a curated toolstack that enables this approach in practice, making it usable by engineers and manageable by organizations.

The following chapters outline the mindset, mechanics, and motivation for elevating documentation to first-class status — and transforming it from a bottleneck into a core enabler of scalable, secure, and maintainable product lines.

Systems Thinking as the Foundation of Engineering Disciplines#

Systems Thinking provides the structural backbone of any mature engineering discipline. It creates a holistic view onto the System under Development together with the Stakeholders and Neighbor Systems and Enabling Systems in the Context of the Product and its Variants, the valid combinations in the configuraation space of the Product Line . All the contextes during the lifetime of the product line are considered, also comprising developing, testing, industralisation and also the graceful end of lifetime. It ensures that the interfaces between systems, subsystems, and domains are clearly defined — not only technically, but also conceptually. This thinking supports traceability, reuse, modularity, and long-term maintainability.

In our context, the relevant disciplines are:

  • Systems Engineering itself,

  • Software Engineering,

  • Hardware Engineering — understood as the union of Mechanical and Electrical Engineering.

Systems Thinking serves as the connective tissue between these fields, enabling engineers to reason across abstraction layers, boundaries, and life-cycle stages.

Critically, mature products — especially those with high demands for functionality, safety, and security — cannot be engineered competitively without proper Systems Engineering. It provides the methods, language, and mindset necessary to coordinate complexity, reduce integration risk, and ensure product quality across all engineering domains.

Reliable and Traceable Communication through Specifications#

In multidisciplinary engineering environments, communication often breaks down at the boundaries — between teams, between domains, and between lifecycle stages. Verbal agreements, chat messages, or undocumented assumptions don’t scale — and they don’t survive audits.

Specifications act as shared contracts. They define expectations, responsibilities, and constraints in a way that is both human-readable and machine-verifiable. When treated as first-class engineering artifacts, specifications provide a communication channel that is reliable, traceable, and auditable.

This makes them indispensable in regulated and quality-driven domains, where decisions must be justified, changes reviewed, and intent preserved — not just over months, but over the entire product lifecycle.

Authoring Specifications with Docs-as-Code: Precise and Efficient#

Engineers write specifications, manuals, handbooks, and guides — not in isolation, but alongside the systems they build. When authored in the same environment and tools used for code development, documentation becomes a natural part of the engineering workflow.

The Docs-as-Code approach enables specifications to be:

  • Version-controlled, allowing change history, merging, branching, comparing and accountability,

  • Peer-reviewed, ensuring quality and shared understanding, and

  • CI-integrated, making validation, formatting, and deployment automatic.

This alignment lowers the barrier to contribution and increases accuracy, because engineers stay in their familiar toolchain. Over time, this leads to a documentation culture that is precise, transparent, and scalable — with fewer handover issues and stronger cross-team collaboration.

Moreover, specifications treated as code can be reused, queried, transformed, and embedded — turning static documents into living, actionable assets.

It’s an all-in-one toolbox, but not a mythical do-it-all solution.

Authoring and communicating information in the form of documents is especially well-suited for describing complex systems, processes, or concepts. When information is highly cohesive—meaning its parts are tightly interrelated—splitting it into isolated items or database entries often disrupts the logical flow and narrative, making it harder for both writers to express and readers to comprehend the full context. Documents allow for nuanced explanations, cross-references, and the preservation of structure, which are essential for conveying intricate ideas effectively and maintaining the integrity of the information.

Rather than attempting to replace specialized tools, Docs-as-Code is designed to integrate seamlessly with systems optimized for managing itemized information—such as change management tools, issue trackers, and requirements databases. In robust engineering workflows, information naturally evolves: as it matures and stabilizes, it may shift between different tools, formats, and levels of formality.

Docs-as-Code supports this in a very high degree since it handles the contents of the documents, so the code of the document information in the same way software engineering handles the code for the software binaries.

AI-Accelerated Brainwork#

Engineers operate in an information flood: books and standards, vendor portals, technology websites, blogs, knowledge bases, Q&A threads, chat archives, and open‑source repositories. Winning teams use Large Language Models (LLMs) as an always-on research and synthesis layer to turn this ocean of material into decisions and actions.

What LLMs make practical at scale:

  • Multisource retrieval and synthesis from long-form texts, specs, READMEs, issues/PRs, and Q&A; answers stay concise and cite sources.

  • Change awareness by scanning release notes and docs to highlight breaking changes and emerging practices.

  • Pattern discovery across repos and discussions to reveal proven approaches and anti‑patterns.

  • Contradiction and gap detection by comparing competing sources and surfacing conflicts or missing assumptions.

  • Task-oriented distillation that converts lengthy material into checklists, decision records, and runbooks.

  • Contextual guidance that ties external guidance to your code and artifacts for “how to apply this here.”

Why this feels native with Docs-as-Code:

  • Text-first artifacts in repositories are directly indexable and linkable, so assistants can read, traverse, and reference them reliably.

  • Stable headings and cross-references act as anchors for precise citations and focused answers.

  • Repository boundaries provide clear scopes for retrieval and sharing, aligning with organizational data policies.

The result: engineers keep pace with the external knowledge landscape, while their documentation remains the place where understanding is consolidated and immediately actionable.

CropTrustful LLM deployments—whether on-premise or with trusted cloud providers under appropriate enterprise plans—must include robust cybersecurity: enforce data residency and retention controls, encrypt data in transit and at rest, apply identity-aware access with auditing, ensure network/model isolation, validate supply chains for models and dependencies, and redact sensitive content in prompts and responses.

HermesBaby: Enabling Docs-as-Code in Corporate Environment#

HermesBaby is best understood as a lightweight but powerful software stack for Docs-as-Code workflows in engineering contexts. It curates and integrates proven tools — rather than reinventing them — and aligns them with engineering discipline and corporate demands.

At its core, HermesBaby emphasizes:

  • MyST Markdown, as the most expressive and future-proof text format for technical documentation,

  • VS Code, as the de facto Integrated Development Environment for modern developers,

  • Draw.io, as the most versatile and accessible diagramming tool across disciplines

  • and Sphinx, as a mature, versatile, extensible and well-adopted docs-as-code build-tool written in Python.

Engineers benefit from structured templates, live previews, and a clean CLI — all within their familiar tools. Meanwhile, HermesBaby integrates cleanly into CI pipelines and IT infrastructure, enabling secure publishing, dependency validation, and access control.

By aligning authoring freedom with operational discipline, HermesBaby enables structured, scalable, and secure documentation workflows — even in regulated environments.

Collaboratively Defined Workflows Empower Engineers#

When engineers participate in shaping the workflows they rely on, they stop seeing process as overhead — and start seeing it as a tool they own.

Collaborative workflow design uncovers friction points early, builds shared understanding, and activates intrinsic motivation. Engineers who help define a process are far more likely to trust and adopt it — not because they were told to, but because they helped build it.

This approach fosters ownership, reduces resistance, and leads to better compliance — not through enforcement, but through alignment. Over time, it nurtures a culture of continuous improvement and self-organization, where workflows evolve with the needs of the team and the product.

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