Linked Knowledge Nuggets: arrow_forward "8 quick wins for a clean requirements analysis"
personAuthor: Process Fellows
Use a clear requirements template, define ownership, maintain traceability, differentiate between functional and non-functional requirements, apply quality checklists, involve stakeholders early, and ensure version control. And yes – always think: Can I test/ verify this?
arrow_forward "AI in Automotive Systems: Aligning with ISO/PAS 8800"
personAuthor: Sebastian Keller
How can AI be safely integrated into the vehicles of tomorrow?
ISO/PAS 8800:2024 lays the foundation for managing artificial intelligence in safety-related automotive systems. Join our webinar and learn what the new specification means for managers, project leaders, quality specialists, and engineering teams.
We’ll demystify the key concepts behind AI safety, explain how ISO/PAS 8800 relates to ISO 26262, and show how organizations can prepare for the next generation of system validation and assurance.
You will gain an overview of how to transfer AI development activities into structured frameworks such as Automotive SPICE, define roles such as AI safety manager or data governance lead, and avoid common pitfalls such as uncontrolled uncontrolled data drift.
Reserve your spot today and lead your company's AI safety transformation with confidence.
personAuthor: Manuel Schmidt, Alexander Feulner
How is modern requirements engineering implemented in practice, and what role does artificial intelligence play across the entire lifecycle—from creation and analysis to the quality assurance of requirements?
Together with our partner Innovigate, the developer of the Tracelane tool, we illustrate this with a practical demonstration. Tracelane is a modern requirements management tool with an intelligent AI assistant that creates, analyzes, evaluates, and provides concrete suggestions for improvement.
school
Webinar recording and slides
arrow_forward "How do I bring levels to my requirement chaos?"
personAuthor: Timo Karasch
First, our processes constantly ask us to specify requirements. Then we have to bother with quite a lot of documents and specifications. Even the relevant literature cannot explain to me in an understandable way what the difference between all these requirements is supposed to be. And finally, an assessor comes and evaluates my specification as insufficient.
This is the end of it! In this webinar we want to bring levels into our requirements chaos. Using simple examples, we will show the different types of requirements and learn a few helpful methods for working with requirements.
school
Webinar recording and slides
arrow_forward "Requirements are not just for engineers"
personAuthor: Process Fellows
Involve testers, safety experts, UX and product managers in the review. Each brings a unique perspective — and helps uncover hidden assumptions.
arrow_forward "What is the difference between need, expectation and requirement?"
personAuthor: Process Fellows
Typically the terms Need, Expectation, and Requirement are distinguished as follows:
A Need describes a fundamental problem or desire of a stakeholder that should be addressed. It is often vague and not directly actionable. Needs arise from the stakeholder’s context, goals, or challenges.
Example: “I want to be able to work faster.”
An Expectation is a stakeholder’s specific idea or assumption about how a Need might be fulfilled. It is subjective and can be expressed explicitly or implicitly.
Example: “I expect the system to automatically suggest options.”
A Requirement is a documented, verifiable, and actionable specification derived from a Need or Expectation. It serves as the basis for design, implementation, and testing.
Example: “The system shall display three relevant suggestions to the user within 2 seconds.”
Summary:
Need: motivates development
Expectation: expresses a concrete idea
Requirement: a clearly defined, verifiable specification describing what is expected to be fulfilled
This distinction helps structure the process of eliciting, analyzing, and documenting requirements.
# PROCESS PURPOSE
The purpose is to refine the machine learning-related software requirements into a set of ML requirements.
# PROCESS OUTCOMES
O1
The ML requirements including ML data requirements are identified and specified based on the software requirements and the components of the software architecture.
O2
The ML requirements are structured and prioritized.
O3
The ML requirements are analyzed for correctness and verifiability.
O4
The impact of the ML requirements on the ML operating environment is analyzed.
O5
Consistency and bidirectional traceability are established between the ML requirements and the software requirements, and between the ML requirements and the software architecture.
O6
The ML requirements are agreed and communicated to all affected parties.
# BASE PRACTICES
BP1
Specify the ML requirements. (
O1 )
Use the software requirements and the software architecture to identify and specify the functional and non-functional ML requirements, as well as the ML data requirements specifying the data characteristics and their expected distributions. Note 1: Non- (Functional requirement = A statement that identifies what a product or process must accomplish to produce required behavior and/or results.) may include relevant characteristics of the ODD and aspects such as robustness, performance, and level of trustworthiness. Note 2: The ML data requirements are input for SUP.11 Machine Learning Data Management but also for other MLE processes. Note 3: In case of ML development only, (Stakeholder requirements = Any type of requirement for the stakeholders in the given context, e.g., customer requirement, supplier internal requirements (product-specific, platform etc.), legal requirements, regulatory requirements, statutory requirements, industry sector requirements, international standards, codes of practice etc. …) represent the software requirements. Note 4: Data characteristics can be, e.g., gender, weather conditions, street conditions within the ODD.
personAuthor: Process Fellows
The INCOSE Guide to Writing Requirements is a comprehensive resource developed by the Requirements Working Group (RWG) of the International Council on Systems Engineering (INCOSE). It provides best practices and structured guidance for writing high-quality requirements in systems engineering. The guide contains
Characteristics of "good" requirements
Writing rules
Boilerplates / templates
Quality aspects for requirement sets
Common pitfalls when specifying requirements
The guide is available for free for INCOSE members, there is a small fee for non-members. The linked summary sheet is available for free for everybody.
school
External link to pdf file published at INCOSE website
arrow_forward "Specifying behavior under all conditions"
personAuthor: Process Fellows
It’s not just about sunny-day scenarios. Requirements should specify as well handling of failure cases, degraded modes, and edge conditions. Ask: what happens if X fails or behaves oddly?
BP2
Structure the ML requirements. (
O2 )
Structure and prioritize the ML requirements. Note 5: Examples for structuring criteria can be grouping (e.g., by functionality) or variants identification. Note 6: Prioritization can be done according to (Project = Endeavor with defined start and finish dates undertaken to create a product or service in accordance with specified resources requirements.) or stakeholder needs via, e.g., the definition of (Release = A product delivered to a customer, including a defined set of functionalities and properties.) scopes. Refer to SPL.2.BP1.
Linked Knowledge Nuggets: arrow_forward "How do I bring levels to my requirement chaos?"
personAuthor: Timo Karasch
First, our processes constantly ask us to specify requirements. Then we have to bother with quite a lot of documents and specifications. Even the relevant literature cannot explain to me in an understandable way what the difference between all these requirements is supposed to be. And finally, an assessor comes and evaluates my specification as insufficient.
This is the end of it! In this webinar we want to bring levels into our requirements chaos. Using simple examples, we will show the different types of requirements and learn a few helpful methods for working with requirements.
school
Webinar recording and slides
BP3
Analyze the ML requirements. (
O3 )
Analyze the specified ML requirements including their interdependencies to ensure correctness, technical feasibility, and ability for machine learning model testing, and to support (Project = Endeavor with defined start and finish dates undertaken to create a product or service in accordance with specified resources requirements.) management regarding (Project = Endeavor with defined start and finish dates undertaken to create a product or service in accordance with specified resources requirements.) estimates. Note 7: See MAN.3.BP3 for (Project = Endeavor with defined start and finish dates undertaken to create a product or service in accordance with specified resources requirements.) feasibility and MAN.3.BP5 for (Project = Endeavor with defined start and finish dates undertaken to create a product or service in accordance with specified resources requirements.) estimates.
Linked Knowledge Nuggets: arrow_forward "How to handle conflicting requirements?"
personAuthor: Process Fellows
Don’t sweep conflicts under the rug. Document them. Bring stakeholders together. Use impact analysis. Ultimately ensure consistency. Clarity today avoids chaos tomorrow.
Systems Engineering aligned with Safety, Security and SPICE
- from a practical point of view
Sponsored
Unclear requirements, missing traceability, and an unstructured architecture can be costly, which is why our training “Systems Engineering aligned with Safety, Security and SPICE – from a practical point of view” demonstrates how structured methods and clear processes enable efficient system development.
BP4
Analyze the impact on the ML operating environment. (
O4 )
Analyze the impact that the ML requirements will have on the interfaces of the (Software component = Software component in design and implementation-oriented processes: The software architecture decomposes the software into software components across appropriate hierarchical levels down to the lowest-level software components in a conceptual model. Software component in verification-oriented processes: The implementation of a SW component under verification is represented, e.g., as source code, object files, library file, executable, or executable model.) and the ML operating environment. Note 8: The ML operating environment is defined as the infrastructure and information which both the trained ML model and the deployed ML model need for execution.
Linked Knowledge Nuggets: arrow_forward "How to handle conflicting requirements?"
personAuthor: Process Fellows
Don’t sweep conflicts under the rug. Document them. Bring stakeholders together. Use impact analysis. Ultimately ensure consistency. Clarity today avoids chaos tomorrow.
BP5
Ensure consistency and establish bidirectional traceability. (
O5 )
Ensure consistency and establish bidirectional traceability between the ML requirements and the software requirements and between the ML requirements and the software architecture. Note 9: Bidirectional traceability supports consistency, facilitates impact analyses of change requests, and verification coverage demonstration. Traceability alone, e.g., the existence of links, does not necessarily mean that the information is consistent. Note 10: Redundant traceability is not intended, but at least one out of the given traceability paths is required.
Linked Knowledge Nuggets: arrow_forward "Consistency vs. Traceability – What’s the Difference?"
personAuthor: Process Fellows
Consistency ensures that related content doesn’t contradict itself – e.g., requirements align with architecture and test. Traceability, in contrast, is about links: can you follow a requirement through to implementation and verification? Both are needed – consistency builds trust, traceability enables control. Typically, traceability strongly supports consistency review.
arrow_forward "How to handle conflicting requirements?"
personAuthor: Process Fellows
Don’t sweep conflicts under the rug. Document them. Bring stakeholders together. Use impact analysis. Ultimately ensure consistency. Clarity today avoids chaos tomorrow.
arrow_forward "The role of traceability in risk control"
personAuthor: Process Fellows
Traceability isn’t just about completeness — it’s about managing impact. When a requirement changes, trace links tell you what’s affected. That’s your early-warning system.
arrow_forward "The true benefit of traceability
"
personAuthor: Process Fellows
Sometimes the creation of traceability is seen as an additional expense, the benefits are not recognized.
Traceability should be set up at the same time as the derived elements are created. Both work products are open in front of us and the creation of the trace often only takes a few moments.
In the aftermath, the effort increases noticeably and the risk of gaps is high.
If the traceability is complete and consistent, the discovery of dependencies is unbeatably fast and reliable compared to searching for dependencies at a later stage, when there may also be time pressure.
It also enables proof of complete coverage of the derived elements and allows the complete consistency check.
BP6
Communicate the agreed ML requirements and the impact on the operating environment. (
O6 )
Communicate the agreed ML requirements, and the results of the impact analysis on the ML operating environment to all affected parties.
# OUTPUT INFORMATION ITEMS
15-51
Analysis results (
O3, O4 )
Results of analysis of an object or (Task = A definition, but not the execution, of a coherent set of atomic actions.).Identifies:
Object under analysis
Analysis criteria
Selection criteria or prioritization scheme
Decision criteria
Quality criteria
Includes:
Decisions and selections performed
Assumptions and constraints
Evaluation criteria, for example correctness, completeness or consistency to a work product
Examples and references:
Verifiability analysis results, for example when a test machine becomes defective
Results of a feasibility analysis
Used by these processes:
ACQ.4 Supplier Monitoring
HWE.1 Hardware Requirements Analysis
HWE.2 Hardware Design
MAN.5 Risk Management
MAN.6 Measurement
MLE.1 Machine Learning Requirements Analysis
MLE.2 Machine Learning Architecture
PIM.3 Process Improvement
SWE.1 Software Requirements Analysis
SWE.2 Software Architectural Design
SYS.1 Requirements Elicitation
SYS.2 System Requirements Analysis
SYS.3 System Architectural Design
13-52
Communication evidence (
O6 )
Evidence of interpersonal communication.Identifies:
Scope of information
Need for feedback, for example an expected confirmation within one week
Meta data, for example time when communication was done or how information was distributed.
Includes:
Personal information
Work-flows, for example within tools
Examples and References:
E-mails and other forms of memos
Verbal statements
Meeting minutes, for example in standups
Electronic media, for example webcasts, blog posts intranet forum
Chat protocols
Wiki pages
Photo protocol
Used by these processes:
ACQ.4 Supplier Monitoring
HWE.1 Hardware Requirements Analysis
HWE.2 Hardware Design
HWE.3 Verification against Hardware Design
HWE.4 Verification against Hardware Requirements
MAN.3 Project Management
MLE.1 Machine Learning Requirements Analysis
MLE.2 Machine Learning Architecture
MLE.3 Machine Learning Training
MLE.4 Machine Learning Model Testing
PIM.3 Process Improvement
REU.2 Reuse of Products
SUP.1 Quality Assurance
SUP.11 Machine Learning Data Management
SWE.1 Software Requirements Analysis
SWE.2 Software Architectural Design
SWE.3 Software Detailed Design and Unit Construction
SWE.4 Software Unit Verification
SWE.5 Software Component Verification and Integration Verification
SWE.6 Software Verification
SYS.1 Requirements Elicitation
SYS.2 System Requirements Analysis
SYS.3 System Architectural Design
SYS.4 System Integration and Integration Verification
SYS.5 System Verification
VAL.1 Validation
Used by these process attributes:
PA2.1 Process performance management process attribute
13-51
Consistency evidence (
O5 )
Evidence of information to be semantically coherent alongrelevant artifacts, ensuring completeness, purpose maturity of processes, (Task = A definition, but not the execution, of a coherent set of atomic actions.) and products throughout their lifecycle.Identifies:
Traceability information, for example hyperlinks, repository location or editorial references.
Naming conventions
Relevant artifacts
Revision and revision history information
Change documentation and analysis information
Includes:
Meta-information, for example database identifiers notes in Git commits comments
Examples and References:
Evidence of Definition of Done (DoD) adherence.
Used by these processes:
HWE.1 Hardware Requirements Analysis
HWE.2 Hardware Design
HWE.3 Verification against Hardware Design
HWE.4 Verification against Hardware Requirements
MAN.3 Project Management
MLE.1 Machine Learning Requirements Analysis
MLE.2 Machine Learning Architecture
MLE.3 Machine Learning Training
MLE.4 Machine Learning Model Testing
SUP.8 Configuration Management
SUP.10 Change Request Management
SWE.1 Software Requirements Analysis
SWE.2 Software Architectural Design
SWE.3 Software Detailed Design and Unit Construction
SWE.4 Software Unit Verification
SWE.5 Software Component Verification and Integration Verification
SWE.6 Software Verification
SYS.2 System Requirements Analysis
SYS.3 System Architectural Design
SYS.4 System Integration and Integration Verification
SYS.5 System Verification
VAL.1 Validation
17-00
Requirement (
O1, O2 )
Statement that identifies an operational, functional, or design characteristic, which is unambiguous, testable, measurable, and necessary for product or process acceptability.Identifies:
Expectation of functions and capabilities (e.g., non- (Functional requirement = A statement that identifies what a product or process must accomplish to produce required behavior and/or results.))
Owner and origin of the requirement
Identification information
Boundaries, for example target price range
Includes:
Stakeholder viewpoint, for example black-box perspective
Design constraints, for example predefined and necessary implementation details.
Tolerances
Domain specific details, for example of safety and privacy standards
Examples and references:
ISO/IEC directive quote: “expression, in the content of a document, that conveys objectively verifiable criteria to be fulfilled and from which no deviation is permitted if conformance with the document is to be claimed”
ISO/IEC 42010:2007
(Hardware = Assembled and interconnected electrical or electronic hardware components or parts which perform analog or digital functions or operations.) level requirements for example mission profile, storage requirements of environment boundary definitions during environment test.
Operational expectations for example power consumption, crank behavior, heat dissipation boundaries.
Resource constraints, for example performance and memory space definition.
Used by these processes:
HWE.1 Hardware Requirements Analysis
MLE.1 Machine Learning Requirements Analysis
SWE.1 Software Requirements Analysis
SYS.1 Requirements Elicitation
SYS.2 System Requirements Analysis
17-54
Requirement Attribute (
O2, O3 )
Information supporting structuring of requirements for specific context or intent.Identifies:
MLE.1
Machine Learning Requirements Analysis
