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Requirements Traceability – Does My Data Model Matter?

Nearly all engineering organizations have one or more initiatives underway to improve their product development process. Live Traceability™, Model-Based Systems Engineering (MBSE), and digital engineering are the most common areas of focus. As engineers look over the fence and make fun of marketing types for being distracted by shiny objects, marketeers look back and see a similar behavior – just with geekier objects like SysML, digital twins, and simulation. The recurring pattern we see is that at some point during the early stages of the initiative, the realization hits that the data model for requirements across teams and projects is highly inconsistent and lacks consistent relationship rules among data objects. It becomes clear at this point that further progress on the initiatives cannot be made without first fixing the inconsistent and lacking data model.

Some teams will resist an effort to establish a consistent core data model. These teams will ask to keep the flexibility to refine their own engineering data shape and that is OK. The keyword is “refine” and not “define.” Having a consistent core data model, that some teams are allowed to refine for themselves, allows for innovation around the engineering process while still enabling process-wide, integration automation, Live Traceability, model-based systems engineering (MBSE), and digital engineering.

Current Requirements Data Model

For most companies, the data model mess came into existence through a project- and document-centric mindset with legacy requirements management tools. Each project team was allowed to modify their own data structures and each set of requirements lived alone as a document in a repository. This provided project teams with flexibility but over time and over dozens, hundreds, or thousands of projects has led to a challenging situation. We often find that teams have defined the same information in numerous different ways and that even within the same teams there is significant variance across documents. In short, the best way to describe the situation is as a repository of thousands of self-contained documents and no data model exists nor even a common definition of objects upon which to achieve Live Traceability, reuse across projects, MBSE, or digital engineering.

Achieving Live Traceability™ with Jama Connect^®

Jama Software^®‘s Live Traceability™ allows engineering teams to quickly and easily access the latest and most complete information for any requirement, no matter the stage of development or tools used. This real-time capability boosts productivity by ensuring teams work with the latest data and reduces risks like delays and defects by finding issues early. Research shows that issues found late can be much more expensive to fix, which is why Live Traceability is so important. Jama Connect^® helps overcome the limitations of older tools, leading to better results in many industries such as automotive, medical devices, aerospace & defense, and more. To learn more, visit Buyer’s Guide: Selecting a Requirements Management and Traceability Solution

What is Necessary to Move Forward

Organizations invest in software tools but have forgotten to invest in their data. A consistent data model is the best way to maximize the benefits of software tooling, but can only be achieved by spending time on analysis.

Jama Software has developed a Data Model Diagnostic™ (DMD) to help tackle this challenge, taking data from your legacy tool (IBM® DOORS®), understanding its shape and size, and transitioning the data into a model-based framework (Jama Connect™). The DMD automates the analysis of the existing documents to determine the most common object definitions upon which to base a consistent data model going forward. Once a data model has been determined, the next step is to implement a model-based, requirements management solution that ensures compliance is maintained. As opposed to a legacy, document-centric requirements management tool, a model-based one ensures consistent application of all objects AND defines and maintains the relationship rules among the objects. This forms a model representation of the requirements in a consistent manner across projects and is a necessary requirement for MBSE and SysML modeling.

To Get Started With Your Free Data Model Diagnostic™ Consultation: CLICK HERE

With the trend of organizations practicing Systems Engineering to move towards what is referred to as “Model-based Systems Engineering” (MBSE), there are various perspectives as to just what is meant by MBSE. Similar to the old story of the blind men and the elephant, MBSE cannot be effectively practiced when viewed from just one perspective (requirements, models, patterns, standards, industry specific application, etc.). To successfully practice MBSE, wise systems engineers recognize and use each perspective as appropriate to their specific needs. Based on these needs, they choose the appropriate capabilities, tools, and visualizations that will meet their needs. One size doesn’t fit all.

The degree to which the data and information is captured and managed is driven by the needs of the organization and projects from a business and technical perspective based on the size and complexity of their systems, their product line, culture, processes, workforce, their diversity and complexity of supply chains, and types of engineering information that comprises a technical baseline for their system of interest.

SysML and other language-based modeling tools

MBSE is a concept that is maturing and means different things to different people. To some practitioners, MBSE is equated to the use of SysML and other language-based modeling tools. With these tools, practitioners can develop detailed analytical, behavior, and architectural models of a system, with a primary focus of functionality, performance, and interactions between various entities defined to be part of the system being modeled. These types of models are useful tools for system analysis, developing simulations, and creating what is referred to as a “digital twin”.

To others, what they are calling MBSE is really Model-based Design (MBD). MBD design starts with a set of requirements and using various language-based modeling tools to architect and design a system, develop simulations to assess the behavior of the system, and then develop a set of design output specifications to which the system is built or coded. Often the MBD activities are completed in a silo separate from the other SE lifecycle process activities.

MBSE is much more than SysML!

The above language-based modeling approaches do not address the true intent of MBSE. While these capabilities can be useful, MBSE is much more than using language-based modeling tool to develop analytical, logical, behavioral, and architectural models.

The goal of an organization, when adopting MBSE, is to move from a document-centric to a data-centric practice of Systems Engineering (SE) so as to realize the real intent of MBSE which is to develop, maintain, and manage a data and information model of the system being developed along with a model of all the system life cycle process activities, resulting artifacts, and their underlying data and information.

MBSE is not really about any particular type of model or visualization of data and information – whether that be a model, diagram, report, document, sets of needs, or sets of requirements – but is about the underlying data and information model that enables consistency across the data and information that represents the various models and visualizations.

The data and information model that must be developed should represent SE artifacts generated during the product development process activities across all lifecycle stages. Information associated with the elicitation of stakeholder needs and requirements, lifecycle concepts definition, analysis, and maturation, deriving an integrated set of textual needs, transforming those needs into sets of textual design input requirements, verification the system meets those requirements, and validation that the system meets the needs must also be captured within the data and information model. In addition, relationships and dependencies of the individual data items must be captured (traceability) in order to help determine and ensure consistency across all lifecycle stages, prove compliance with standards and regulations, as well as help assess the impact of changes made to any of the data items across all lifecycle stages.

Can my organization adopt MBSE without using SysML or other language-based tools?

Sadly, there are many organizations that have a misconception concerning what the true intent of MBSE is – as a result they think that MBSE is not for them.

In a recent conversation with a system engineer, I was told that her organization was not going to adopt MBSE because they don’t see the utility of developing complex SysML models of their products. In addition, she noted that to do so would require a change in how their managers and engineers think based on the SysML standard as to how to construct models and the specific terminology used. To them SysML modeling is not intuitive, has a large learning curve, and has little apparent utility and return on investment – for their organization, especially for their product line that has a minimum amount of software and complexity.

My response to her was: MBSE is not SysML!

I told her what the true intent of MBSE is to practice systems engineering with a data-centric perspective, establishing the capability to capture, manage, access data, and manage the interrelationships between SE work products can be accomplished through a variety of methodologies, which range from the establishment of a single relational database to a virtually integrated, but distributed, database by means of a federation (or data map/index) of disparate data sources.

The resulting data and information model can be captured using a variety of SE tools and applications. To effectively manage our ever increasing complex, systems there are benefits to managing this underlying data and information in such a way it can be shared across the system life cycle process activities, shared between the various SE tools used to create and manage this data and information, and shared between organizations involved in the development and operations of the system of interest. This sharing will help ensure correctness, consistency, and completeness of the data and information typical of our ever increasingly complex systems as well as enable collaboration between not only the project team members, but also external stakeholders involved in the development of the system.

Parting Thoughts

The answer to the question: “Can my organization adopt MBSE without using SysML or other language-based tools?” is YES!

Since one size doesn’t fit all, an organization must assess their needs and produce an MBSE solution that best fits its domain, product line (degree of complexity), and culture. As a minimum, they need to establish a capability to define and manage needs, requirements, verification, and validation across the system lifecycle. These capabilities will allow the organization to build a data and information model of their products and systems engineering process activities and artifacts.

Based on these needs and desired capabilities, the organization can choose the appropriate SE toolset, update their processes, and train their people in these tools and processes.

Stay tuned for more to come from Lou Wheatcraft in the coming months.

My initial intention for this blog was to talk about what I describe as MBSE tool maturity levels. And I will surely get around to those levels. But first I think it is important to draw attention to the definitions of MBSE that experts use and offer some observations that might be helpful to those who are beginning their own MBSE initiatives.

What is MBSE?

The International Council on Systems Engineering (INCOSE) describes MBSE as the “formalized application of modeling to support system requirements, design, analysis, verification, and validation activities beginning in the conceptual design phase and continuing through-out development and later lifecycle phases.” This is simple enough to digest and the only noticeable difference between it at their definition of systems engineering is the reference to “formalized application of modeling.” They do not however, define what “modeling” means.

NASA says MBSE “involves applying software-based tools to capture systems engineering evidence — typically called “artifacts” — in a systematic, disciplined way that allows people to manage complexity while communicating effectively across the life cycle of a system.

MBSE is to perform systems engineering – connects system relationships
Controlling system configurations
Communicates an overall system picture accessible to all
Makes it easier to integrate disparate material
Ensures everyone is working on the same up to date material at all times
Eliminates problems with version control

RELATED: MBSE Made Easy – Overcoming the Organizational Challenges

What is a Model?

NASA’s definition certainly provides a good deal of information, but they don’t even use the word “model” in their description. Lenny Delligatti, one of the most respected MBSE experts in the world wrote that, “A modeling method is something like a road map; it’s a documented set of design tasks that a modeling team performs to create a system model. More precisely, it’s a documented set of design tasks that ensures that everyone on the team is building the system model consistently and working toward a common end point. Without such guidance, there will be wide variance in the breadth, depth, and fidelity that each member of the team builds into the system model.” What in simple terms is a model then? Wikipedia tells us that a “model is an informative representation of an object, person or system.” These definitions are super informative.

An observation I have is that organizations instantly equate MBSE with the use of a SysML tool. The experts tell us however, that MBSE is the combination of methods to create a representation. None of them say that to perform MBSE one must adopt a graphical modeling tool. There are many different methods to create models/representations. Some are graphical and some are textural. At the heart of MBSE is the drive to move from static documents (Word and Excel) into an information-based paradigm. The table below eloquently articulates the differences and standout benefits of using an MBSE approach.

Table source: Laura Hart, Lockheed Martin

MBSE Tool Maturity Levels

There are certainly many organizations that are still performing systems engineering only using documents. For the rest, there are a plethora of tools in the ecosystem used to create models including tools that support SysML. To begin the journey of MBSE, you don’t have to adopt a SysML tool right away. Maturity of MBSE is still young. Very few organizations do it for a program in an end-to-end manner. The learning curve is steep. Generally, a few engineers become the “scribe” and then end up translating to the extended team. And keep in mind a systems engineer is a “cat herder.” This person is the one who sees the big picture, understands the problem, and is the translator across engineering domains and the business. A SysML tool won’t be the only tool in their arsenal.

This finally brings me to describing MBSE tool maturity levels. Many organizations are already performing MBSE without even calling it MBSE just by the tools and processes they use in the organization. Level 0 is what I describe as only using documents and having no model representations at all. Level 1 is barely a step up from level 0 but the have a requirements tool in place or possibly a Kanban board that is capturing loosely coupled relationships between data. Level 2 is where teams are now combining the use of the RM tool with diagrams. Perhaps they are drawing diagrams in Visio or a UML tool. Level 3 is the combination of requirements, architectures, diagrams are used in conjunction with a defined methodology for how the information is defined, related, and analyzed. The tool will programmatically constrain the data in order to provide consistency as well as provide the mechanisms to define once and then reuse. Level 4 tool maturity is where organizations use a requirements tool and SysML tool.

Jama Connect aligns as Level 3 maturity. Customers who use Jama Connect experience their requirements becoming a source for strategic information during all stages of the system lifecycle. Requirements are turning into actionable data. The software world with its mass adoption of agile practices is trickling into the systems space and requirements now take different shapes. MBSE takes more than use of a language-based graphical modeling tool. Even for a Level 4 maturity, natural language is still the great common denominator. Not everyone will be an engineer and able to interpret models. Requirements and the structured architecture within Jama Connect provides the necessary context. Jama Software’s Traceable MBSE™ approach can be seen as the bridge one uses to leave behind documents and move towards Level 4 maturity.