How the Rise of Software-Defined Hardware Is Changing Requirements Ownership
The Product Is No Longer What You Ship in a Box
For most of the history of aerospace, defense, and industrial engineering, the product was the hardware. Software was firmware — a thin layer of control logic embedded in the device to make the hardware behave as specified. The requirements hierarchy made sense in that world: system requirements flowed to subsystem requirements, which flowed to hardware requirements, which flowed to software requirements. Hardware was primary. Software served it.
That model is collapsing.
In a modern electric vehicle, the powertrain, thermal management, active safety, and user interface are all principally defined by software. The hardware — inverters, battery modules, compute platforms — changes on a three-to-five-year cycle. The software changes every few weeks via over-the-air update. In a next-generation satellite, the mission payload may be entirely reconfigurable through onboard software running on radiation-hardened FPGAs. In avionics, the flight management and display systems on aircraft already in service are being updated with new software builds that change operational capability without touching a single piece of hardware.
The product is the software stack. The hardware is the platform it runs on.
This inversion has profound consequences for how requirements are structured, who owns them, and how traceability is maintained across the system lifecycle. Most programs have not caught up.
What the Traditional Model Got Right — and Where It Breaks
The classical requirements hierarchy — mission concept to system to subsystem to component — was designed for a world where the dominant design decisions were physical. Allocating mass, power, thermal budget, and structural load across a vehicle structure. Once those allocations were made, software inherited what was left.
This worked for two reasons. First, hardware and software shared a release cadence: both were frozen before delivery, so requirements traceability could be closed at a single point in time. Second, the system’s operational behavior was bounded by what the hardware could physically do. Software could not make an actuator move faster than its mechanical limits, so tracing a performance requirement through hardware and into a software control law was a coherent chain.
Neither of those conditions holds in software-defined systems.
A software-defined vehicle’s highway autopilot feature is updated independently of its chassis hardware. The feature has functional requirements — lane-keeping accuracy, speed adaptation range, emergency override response time — that must remain traceable to both the hardware platform’s sensor suite capabilities and the software stack’s functional architecture. When the hardware was designed, many of those features did not exist. When the software feature is updated, the hardware has not changed. The traceability chain must span two development timelines that are structurally decoupled.
The result, in programs that have not adapted, is predictable: the hardware team owns requirements down to their component interfaces, the software team owns requirements for their application layer, and nobody formally owns the integration layer where the product’s behavior is actually defined. Audit findings cluster at exactly that seam.
The Organizational Tension Is the Real Problem
The technical challenge of managing bidirectional requirements between a hardware platform and a software stack is solvable. The organizational challenge — deciding who has authority over requirements that span both — is harder.
In automotive programs pursuing SAE Level 2+ automation, the collision happens between chassis systems engineering (which owns sensor placement, compute platform selection, and hardware interface definitions) and software product engineering (which owns feature requirements written against customer expectations and regulatory targets). Both groups have legitimate claim to the requirements that sit at their boundary: camera field-of-view requirements, radar resolution requirements, compute latency budgets.
What actually happens in most programs: requirements at the interface are owned by neither team formally, managed by both teams informally through emails and meeting notes, and reconstructed at audit time from whatever artifacts can be found. The traceability model is nominally compliant and operationally hollow.
In avionics, the problem takes a different form because DO-178C and DO-254 impose explicit traceability obligations on both software and hardware. But those standards were written with a clear boundary between hardware design assurance and software design assurance. Software-defined avionics products — particularly those using FACE-conformant software or DO-330 qualified tool chains to enable software reuse across platforms — are forcing programs to define requirements at the hardware-software interface that neither standard explicitly owns. The FAA’s own guidance documents acknowledge this gap. Programs are resolving it inconsistently, platform by platform.
In satellite programs, the situation is sharpest. New-generation commercial LEO constellations are designed from the start for in-orbit software updates to mission processing payloads. The spacecraft bus has one set of interface requirements. The payload software stack has another. The interface between them — the data interfaces, processing resource budgets, power envelopes available to the payload — is a third requirements domain that is simultaneously too hardware-specific for the software team to own and too software-dependent for the hardware team to own. Leading operators are inventing new organizational roles — platform systems engineers, payload integration leads — specifically to own this gap.
How Leading Programs Are Restructuring
The programs getting this right share a structural insight: requirements ownership must follow the architecture, not the org chart.
The platform-payload model. Several avionics and satellite programs have adopted an explicit two-domain model. The hardware platform generates a set of interface requirements — what it provides, what it requires, what it bounds. These are called platform requirements. The software product generates a set of capability requirements — what it must do for the operator or end user. Payload requirements. The integration domain, explicitly named and owned by a dedicated systems function, defines the contracts between them. This is not novel as an architecture pattern; it is novel as a requirements ownership pattern. The key move is making the interface domain a first-class artifact with named ownership, rather than treating it as the implied intersection of two other domains.
Feature-level traceability decomposition. In automotive, several OEMs pursuing software-defined vehicle architectures have restructured their requirements databases around features rather than subsystems. A feature — highway autopilot, for example — has a top-level feature requirements document that explicitly references both hardware capability bounds (from the hardware platform team) and software functional requirements (from the software product team). Feature systems engineers own that document and arbitrate conflicts between the two contributing domains. This mirrors how software product companies manage product requirements, applied to a regulated hardware-software system.
Decoupled release traceability. Because hardware and software release on different cadences, traceability models must explicitly handle version-decoupled configurations. Programs at the leading edge are maintaining two parallel traceability chains — one for the hardware configuration baseline, one for the software configuration baseline — with a managed interface that specifies which hardware baseline each software release is verified against. This sounds obvious stated plainly. Most programs do not do it systematically; they discover the need after their first field software update breaks something that hardware verification had already closed.
What Modern Tooling Makes Possible
Legacy requirements management tools were designed around document management. A requirement is a text artifact. Traceability is a link between two text artifacts. The tool enforces that the links exist; it has no model of what the links mean.
That architecture is adequate for a system where requirements are authored once and traced once before delivery. It breaks under software-defined system conditions, where requirements must be maintained across independent release cadences, traced against two separate product baselines, and updated continuously as features evolve.
Graph-based requirements models handle this structurally better. When requirements exist as nodes in a connected graph — with typed relationships that distinguish “derives from,” “allocates to,” “verified by,” and “constrained by” — it becomes possible to query the requirement space in ways document links cannot support. Which hardware interface requirements are affected if I change this software feature requirement? Which software features are currently verified against a hardware baseline that has since been superseded? These are operational questions that programs need to answer in hours, not weeks.
Tools like Flow Engineering implement exactly this model. Rather than treating requirements as documents with links, Flow Engineering represents the requirements structure as a live graph, with explicit relationship typing and the ability to propagate change impact across the model automatically. In the platform-payload architecture described above, this means a change to a platform interface requirement can immediately surface which payload software requirements are potentially affected — before a configuration review board meeting, not after. That shift from reactive impact assessment to proactive impact visibility is the practical value of graph-based traceability in software-defined system programs.
Flow Engineering is also built specifically for the kind of team structure that software-defined programs are converging on: cross-functional, working across hardware and software domains simultaneously, needing a shared model rather than separate document silos. That design intent is visible in how the tool handles requirement ownership — assigning ownership to nodes in the graph rather than to document folders, which directly supports the platform-payload ownership model without requiring organizational workarounds.
The limitation to be honest about: Flow Engineering is purpose-built for systems and hardware engineering contexts, and programs with extremely large legacy document repositories in IBM DOORS or Jama Connect face a non-trivial migration to a graph-native model. That is a real transition cost. For programs starting new architectures or standing up new software-defined vehicle platforms from scratch, it is a non-issue.
What Systems Engineers Need to Do Differently
The organizational and tooling shifts above only work if the role of systems engineering itself adapts.
In the classical model, the systems engineer was primarily a requirements author: decomposing mission-level requirements into allocations, writing interface control documents, populating the RTM. In a software-defined system program, that role does not disappear, but it moves. Requirements are increasingly authored by domain teams — feature teams in automotive, payload teams in satellite, application teams in avionics. The systems engineer’s job is not to write those requirements but to arbitrate them: to maintain the contracts between the platform and payload domains, to flag when a software feature requirement implies a hardware capability that does not exist, to manage the traceability across decoupled release timelines.
This is a different skill profile. It requires comfort with software architecture, not just hardware systems. It requires the ability to read a software interface specification and understand its hardware implications. It requires knowing how to maintain a graph-based requirements model under continuous change, not just populate a matrix before delivery.
Programs that are succeeding in software-defined system transitions have elevated systems engineering to a continuous arbitration function rather than a phase-gated compliance function. They are hiring systems engineers with software backgrounds into hardware programs, and hardware backgrounds into software organizations, deliberately. The organizational seam that requirements fall into is a people problem as much as a tooling problem.
Honest Assessment
The shift to software-defined hardware is not a future state — it is the current reality in automotive, avionics, and satellite programs. The requirements and traceability implications of that shift are not speculative; they are showing up as audit findings, schedule slips at integration, and certification delays.
The programs managing it well share three properties: they have explicitly named and owned the interface between hardware platform and software product as a distinct requirements domain, they have moved to graph-based traceability models that can handle version-decoupled baselines, and they have restructured systems engineering as a continuous arbitration function rather than a phase-gated deliverable function.
The programs managing it poorly are trying to apply a document-centric, org-chart-based requirements model to a system whose architecture no longer maps to that model. The tooling is not the hard part. The organizational willingness to assign explicit ownership to the uncomfortable middle layer is.
That middle layer — the hardware-software interface, the platform-payload contract, the integration domain — is where software-defined products either hold together or fail. Owning it is not optional.