Requirements Engineering in the Age of AI

Requirements Engineering is evolving from manual, experience-driven practices to AI-supported workflows that improve efficiency, consistency and quality while keeping engineers responsible for the final engineering outcome.

1. The Evolution of Requirements Engineering

For decades, Requirements Engineering has relied on structured engineering practices supported by experienced professionals. Engineers work closely with stakeholders to understand their needs, document requirements and ensure that specifications are complete, consistent and suitable for development.

Traditional Requirements Engineering is built on:

• Human expertise and engineering judgment
• Stakeholder collaboration and communication
• Reviews and inspections
• Quality checklists and engineering guidelines
• Manual analysis and traceability

These practices continue to form the foundation of Requirements Engineering and remain essential for delivering successful systems.

As products become increasingly software-driven and interconnected, Requirements Engineering must address challenges that extend beyond the capabilities of purely manual processes.

Modern engineering projects are characterized by:

• Increasing system complexity
• Larger and more interconnected specifications
• Multiple stakeholders with diverse expectations
• Shorter development and release cycles
• Growing safety, security and regulatory requirements

Managing thousands of requirements, maintaining consistency across multiple engineering teams and assessing the impact of changes have become significant challenges that demand more efficient engineering approaches.

2. The Role of AI in Requirements Engineering

Artificial Intelligence is transforming Requirements Engineering by enhancing the way engineers perform their daily activities. Rather than replacing engineering expertise, AI serves as an intelligent assistant that automates repetitive tasks, analyzes large volumes of information and provides valuable insights that support engineering decisions.

AI is particularly effective in activities that involve processing and analyzing engineering information at scale. It can support Requirements Engineering by:

• Detecting ambiguous, incomplete or inconsistent requirements.
• Identifying duplicate, conflicting or related requirements.
• Retrieving relevant requirements, standards and engineering knowledge through semantic search.
• Improving requirement wording and checking compliance with writing guidelines.
• Supporting traceability between requirements, design elements and test cases.
• Assessing the impact of proposed requirement changes.

While AI excels at processing information, successful Requirements Engineering continues to rely on the expertise and judgment of experienced engineers. Engineers provide capabilities that cannot be automated, including:

• Domain knowledge to understand the application and its constraints.
• Context awareness to interpret requirements within the broader system.
• Engineering judgment to evaluate alternatives and make informed decisions.
• Creativity and innovation to develop effective solutions for complex problems.
• Ethical responsibility when balancing safety, security, regulatory and business considerations.
• Stakeholder communication to understand needs, negotiate priorities and build consensus.

By combining the analytical capabilities of AI with human expertise, organizations can improve requirement quality, reduce manual effort and respond more effectively to the increasing complexity of modern engineering projects.

Like any engineering tool, AI can produce incorrect or incomplete results. Engineers remain responsible for reviewing AI-generated outputs, validating their correctness and approving all engineering decisions before they are incorporated into the final specification.

3. AI Throughout the Requirements Engineering Lifecycle

Artificial Intelligence can provide valuable assistance throughout the entire Requirements Engineering lifecycle, supporting each activity with capabilities tailored to its objectives. While AI provides analysis and recommendations, engineers remain responsible for engineering decisions, stakeholder communication and approval of engineering artifacts.

Elicitation

During elicitation, AI assists engineers in capturing and organizing stakeholder information before, during and after stakeholder interactions.

AI can support:

• Interview preparation
• Question generation
• Meeting summarization
• Requirement extraction
• Duplicate requirement detection

Engineers remain responsible for:

• Understanding stakeholder intent
• Clarifying ambiguities
• Resolving conflicting expectations
• Negotiating priorities and scope

Analysis

During analysis, AI helps engineers evaluate the quality, consistency and relationships of requirements across the specification.

AI can support:

• Ambiguity detection
• Consistency checking
• Requirement classification
• Dependency analysis
• Impact analysis
• Conflict identification

Engineers remain responsible for:

• Technical decision-making
• Evaluating design alternatives
• Assessing feasibility
• Performing trade-off analysis
• Risk assessment

Documentation

AI improves the efficiency and consistency of documenting requirements while supporting compliance with engineering standards and organizational guidelines.

AI can support:

• Requirement rewriting
• Standardized wording
• Attribute completion
• Template generation

Engineers remain responsible for:

• Approving the specification
• Applying engineering judgment
• Ensuring technical correctness
• Using appropriate domain terminology

Verification

AI accelerates requirement verification by automatically evaluating specifications against predefined quality rules and engineering criteria.

AI can support:

• Syntax verification
• Consistency checking
• Completeness checking
• Testability assessment
• Traceability verification
• Compliance with writing guidelines

Engineers remain responsible for:

• Reviewing verification findings
• Resolving identified issues
• Defining verification criteria
• Approving the verified specification

Validation

AI assists engineers in determining whether the documented requirements adequately represent stakeholder expectations and intended system behavior.

AI can support:

• Missing requirement detection
• Traceability suggestions
• Scenario generation
• Use case analysis
• Requirement coverage analysis

Engineers remain responsible for:

• Confirming stakeholder expectations
• Validating business objectives
• Approving requirement changes
• Accepting the final specification

Management

As projects evolve, AI helps maintain consistency across changing specifications by supporting impact analysis, organization and traceability.

AI can support:

• Change impact prediction
• Similar requirement search
• Automatic classification and tagging
• Requirement metrics and reporting
• Traceability maintenance

Engineers remain responsible for:

• Configuration management
• Baselining
• Change approval
• Release planning
• Change prioritization

Artificial Intelligence supports every stage of the Requirements Engineering lifecycle by reducing manual effort, improving consistency and providing intelligent recommendations. The greatest value is achieved when AI complements established engineering practices, allowing engineers to focus on collaboration, critical thinking and informed decision-making while retaining full responsibility for the final engineering outcome.

4. Key Takeaways

• Requirements Engineering remains a human-centered discipline, where engineers are responsible for understanding stakeholder needs and making engineering decisions.
• AI supports every phase of the Requirements Engineering lifecycle, from elicitation and analysis to documentation, verification, validation and change management.
• AI excels at automating repetitive tasks and analyzing large specifications, enabling earlier detection of quality issues and reducing manual effort.
• Human expertise remains essential for providing domain knowledge, engineering judgment, creativity, ethical responsibility and effective stakeholder communication.
• AI-generated results should always be reviewed and approved by engineers, who remain accountable for the correctness and quality of the final engineering artifacts.
• The greatest value is achieved when AI and engineers work together, combining the speed and analytical capabilities of AI with the experience and decision-making skills of engineering professionals.

AI in Functional Safety: Opportunities, Challenges and Engineering Responsibility

Can Artificial Intelligence accelerate Functional Safety activities without compromising safety? Discover where AI can support hazard analysis, safety requirements, verification and documentation, the limitations engineers must understand, and why human responsibility remains fundamental in safety-critical development.

1. Why Functional Safety Cannot Ignore AI

Functional Safety has always relied on rigorous engineering processes to ensure that systems operate safely, even in the presence of faults. Standards such as ISO 26262 define structured activities that help engineers identify hazards, derive safety requirements, develop safety concepts and demonstrate that safety objectives have been achieved.

As modern systems become increasingly software-driven and interconnected, these activities continue to grow in both scope and complexity. Safety engineers are expected to manage significantly more information while maintaining the same high level of rigor and traceability.

Today’s Functional Safety projects are characterized by:

• Increasing software and system complexity
• Growing numbers of safety requirements
• More comprehensive safety analyses
• Larger volumes of engineering documentation
• Increasing pressure for shorter development cycles

These challenges require engineers to analyze large amounts of information, maintain consistency across numerous engineering artifacts and ensure complete traceability throughout the safety lifecycle. As projects scale, many traditional engineering activities become increasingly time-consuming and difficult to perform manually.

Functional Safety is therefore becoming a knowledge-intensive engineering discipline. Developing a safety-compliant product requires engineers to combine information from hazard analyses, safety concepts, technical architectures, software requirements, verification results, standards and safety cases, while ensuring that all artifacts remain complete, consistent and traceable.

Artificial Intelligence offers the potential to support many of these activities. By rapidly analyzing large volumes of engineering information, identifying inconsistencies, assisting with documentation and improving traceability, AI can significantly reduce manual effort and help engineers focus on activities that require engineering judgment and safety expertise.

2. Where AI Can Support Functional Safety

Artificial Intelligence can provide valuable assistance throughout the Functional Safety lifecycle by automating repetitive activities, analyzing large volumes of engineering information and improving the consistency of safety artifacts. While AI can accelerate many engineering tasks, responsibility for safety decisions remains with qualified Functional Safety engineers.

Item Definition

The Item Definition establishes the foundation of the Functional Safety lifecycle by describing the system, its intended functionality and its operational environment. AI can help engineers understand complex systems and organize engineering information more efficiently.

AI can support:

• System understanding and summarization
• Interface extraction from specifications
• Identification of operational scenarios
• Documentation generation and refinement

Hazard Analysis and Risk Assessment (HARA)

The HARA identifies potential hazards, evaluates associated risks and determines the required Automotive Safety Integrity Level (ASIL). AI can assist by analyzing large specifications and suggesting areas that require further investigation.

AI can support:

• Hazard brainstorming
• Identification of operational situations
• Malfunction identification
• Consistency checking across safety artifacts

Functional Safety Concept

The Functional Safety Concept defines the safety goals and the functional safety requirements needed to achieve them. AI can improve efficiency by assisting with documentation, traceability and completeness analysis.

AI can support:

• Deriving candidate safety goals
• Drafting Functional Safety Requirements (FSRs)
• Completeness checking
• Traceability between hazards, safety goals and requirements

Technical Safety Concept

The Technical Safety Concept translates functional safety requirements into technical solutions and allocates them to system elements. AI can assist engineers in exploring alternative implementations and identifying potential inconsistencies.

AI can support:

• Architecture exploration
• Safety mechanism suggestions
• Interface consistency analysis
• Allocation proposals for Technical Safety Requirements (TSRs)

Software Safety

Software development typically produces a large number of safety-related requirements, design elements and implementation artifacts. AI can assist by improving quality and automating many review activities.

AI can support:

• Requirement quality analysis
• Code review assistance
• MISRA guideline checking
• Static code analysis
• Unit test generation

Verification

Verification demonstrates that safety requirements have been correctly implemented throughout the development lifecycle. AI can improve verification efficiency by supporting analysis, review and test preparation.

AI can support:

• Test case generation
• Coverage analysis
• Traceability verification
• Review assistance

Safety Case

The Safety Case consolidates the evidence demonstrating that the product satisfies its Functional Safety objectives. Maintaining consistency across numerous engineering artifacts is often one of the most time-consuming activities.

AI can support:

• Documentation generation
• Evidence collection and organization
• Consistency checking across Safety Case artifacts

3. Engineering Challenges

Artificial Intelligence offers significant opportunities to improve Functional Safety engineering, but it also introduces challenges that must be carefully managed. Unlike conventional engineering tools, AI generates recommendations based on learned patterns rather than deterministic algorithms. As a result, engineers must understand not only what AI can do, but also where its limitations may affect safety-critical development.

Hallucinations

AI systems can produce outputs that appear technically correct while containing incorrect, incomplete or fabricated information. In the context of Functional Safety, such hallucinations may lead to incorrect safety arguments, inappropriate safety requirements or misleading recommendations if they are accepted without engineering review.

AI-generated safety artifacts should therefore always be treated as engineering recommendations rather than verified facts.

Missing Context

Functional Safety decisions are rarely based on a single document. They depend on system architecture, operational assumptions, previous engineering decisions, applicable standards and project-specific constraints.

When this context is incomplete or unavailable, AI may generate recommendations that appear reasonable but do not reflect the actual system or safety objectives. Information that exists only in engineers’ experience or undocumented project knowledge cannot be reliably incorporated into AI-generated results.

Explainability

Functional Safety requires engineering decisions to be justified, documented and traceable. Engineers must be able to explain why a hazard was identified, why a particular safety mechanism was selected or why an ASIL classification was assigned.

While AI can generate useful recommendations, it often cannot provide the level of engineering justification required for safety-critical decisions. This lack of explainability makes independent review and engineering validation essential before AI-generated outputs can be incorporated into safety artifacts.

Qualification

ISO 26262 requires software tools to be evaluated and, where necessary, qualified to provide confidence that they do not introduce or fail to detect errors during development.

Artificial Intelligence presents a new challenge because it does not behave like traditional deterministic software tools. The same prompt may produce different outputs, and the underlying decision-making process is based on statistical models rather than explicitly defined algorithms. These characteristics make conventional tool qualification approaches difficult to apply.

As AI becomes more deeply integrated into safety engineering, new methods for evaluating, governing and potentially qualifying AI-assisted engineering tools are likely to become an important area of research and standardization.

4. The Future Safety Engineer

Artificial Intelligence is changing the way Functional Safety activities are performed, but it is not changing their purpose. As AI becomes more capable of analyzing information, generating documentation and identifying inconsistencies, safety engineers will spend less time on repetitive engineering tasks and more time on activities that require technical expertise, critical thinking and professional judgment.

AI is particularly effective at processing large volumes of engineering information, allowing engineers to reduce the effort spent on manual analysis and administrative work.

Future safety engineers may spend less time:

• Writing and maintaining engineering documentation
• Searching standards, guidelines and previous project artifacts
• Reviewing thousands of safety requirements for quality and consistency
• Maintaining traceability across engineering artifacts
• Preparing review reports and supporting documentation
• Collecting and organizing Safety Case evidence

Instead, engineers will increasingly focus on activities where human expertise remains essential.

Future safety engineers will spend more time:

• Making engineering decisions
• Defining safety concepts and architectures
• Evaluating hazards and assessing risk
• Balancing technical, safety and business trade-offs
• Validating AI-generated engineering artifacts
• Collaborating with stakeholders and multidisciplinary engineering teams

Although AI can support almost every phase of the Functional Safety lifecycle, the responsibility for safety decisions remains firmly with qualified safety professionals.

Throughout the safety lifecycle, engineers continue to be responsible for:

Item Definition

• Defining system boundaries and intended functionality
• Confirming operational scenarios and assumptions

Hazard Analysis and Risk Assessment (HARA)

• Identifying hazards
• Assessing Severity, Exposure and Controllability
• Determining ASIL classifications

Functional Safety Concept

• Defining Safety Goals
• Approving Functional Safety Requirements
• Evaluating the effectiveness of safety concepts

Technical Safety Concept

• Selecting safety mechanisms
• Making architectural decisions
• Allocating Technical Safety Requirements

Software Safety

• Approving safety-related requirements
• Reviewing software architecture and implementation
• Evaluating software safety mechanisms

Verification and Validation

• Defining verification strategies
• Reviewing verification results
• Confirming that safety requirements have been correctly implemented and validated

Safety Case

• Assessing safety evidence
• Demonstrating compliance with applicable standards
• Approving the overall safety argument

Artificial Intelligence is changing how Functional Safety engineering is performed, but not who is responsible. As AI automates routine engineering tasks, the value of the safety engineer increasingly lies in making informed decisions, exercising sound engineering judgment and taking responsibility for the safety of the final product.

5. Key Takeaways

• Artificial Intelligence can support Functional Safety throughout the entire safety lifecycle, improving efficiency in activities such as analysis, documentation, traceability and verification.
• AI is most effective as an engineering assistant, helping engineers process large volumes of information, identify inconsistencies and automate repetitive engineering tasks.
• Safety decisions remain the responsibility of qualified Functional Safety engineers. Activities such as hazard analysis, ASIL determination, safety concept development and safety approval require engineering judgment that cannot be delegated to AI.
• AI-generated safety artifacts must be reviewed, verified and validated before they become part of the safety lifecycle or Safety Case.
• The use of AI introduces new engineering challenges, including hallucinations, limited explainability, incomplete context and the qualification of AI-assisted engineering tools.
• The future of Functional Safety combines AI-assisted engineering with human expertise, allowing engineers to focus less on manual documentation and more on engineering decisions, risk evaluation and ensuring the safety of the final product.
• Artificial Intelligence does not change the principles of Functional Safety. It changes how engineers perform safety activities while preserving the rigor, traceability and accountability required for safety-critical systems.

Production-ready Code from High-quality Requirements

Can Requirements Engineering become the programming language of the future? As AI transforms software development, the quality of generated code increasingly depends on the quality of the requirements. Explore how precise, complete and verifiable requirements could enable AI to generate production-ready software and the engineering challenges that still stand in the way.

1. A New Engineering Vision

For decades, software development has followed a well-established engineering lifecycle. Stakeholder needs are transformed into requirements, requirements into designs, designs into software, and software is finally verified and validated before deployment.

The traditional engineering flow can be summarized as:

This structured approach has enabled the development of increasingly complex and reliable software systems across countless industries.

Artificial Intelligence now introduces a new and potentially transformative possibility:

At first glance, this idea appears both exciting and controversial. Can AI really replace large parts of the traditional software development process? More importantly, can it generate software that is robust, maintainable and ready for production?

Recent advances in Large Language Models demonstrate that AI is already capable of generating functions, APIs, unit tests and even complete applications from natural language descriptions. As a result, the discussion is no longer centered on whether AI can write code. That capability has already been demonstrated.

The more fundamental engineering question is whether the input provided to AI is sufficiently precise to produce the correct implementation. Requirements were originally written to communicate with engineers. If AI is expected to generate production-ready software, those same requirements must also become an unambiguous and complete specification that leaves no room for interpretation.

This represents a significant shift in software engineering. Rather than focusing primarily on the implementation itself, engineering effort increasingly moves toward defining the desired system behavior with greater precision and completeness. The quality of the generated software becomes directly dependent on the quality of the engineering knowledge captured in the specification.

The challenge is no longer simply writing code. It is writing engineering knowledge that is precise enough for AI to transform into production-ready software.

2. Why Requirements Become the Programming Language

Software developers have traditionally transformed requirements into software by interpreting specifications, making design decisions and implementing the resulting solution. When information is missing or unclear, engineers naturally compensate by applying:

• Experience
• Assumptions
• Discussions with stakeholders
• Engineering intuition

Although this approach often produces successful systems, it also introduces variability. Two experienced developers may interpret the same requirement differently and arrive at different implementations.

Artificial Intelligence does not compensate for incomplete specifications in the same way. It generates code from the information available, inferring missing details from learned patterns rather than engineering intent. As a result, every ambiguity, omission or inconsistency increases the likelihood of incorrect or inconsistent implementations.

To generate production-ready software, AI requires requirements that describe the system with a much higher level of precision. Rather than expressing only the expected functionality, the specification should define the engineering knowledge needed to implement the solution correctly, including:

• Functional behavior
• States and state transitions
• Events and triggering conditions
• Interfaces and data exchanges
• Constraints
• Error handling
• Non-functional requirements

As requirements become more complete and precise, they begin to resemble a programming language, not because of their syntax, but because they describe system behavior with sufficient accuracy for consistent implementation. Instead of serving only as documentation for engineers, they become an executable engineering specification that can drive both human and AI-assisted development.

Better requirements produce better code. The quality of AI-generated software is determined first and foremost by the quality of the requirements from which it is generated.

3. What AI Needs to Generate Production-ready Code

Artificial Intelligence can generate impressive source code, but only when it receives a sufficiently complete and unambiguous specification. Unlike human developers, AI cannot ask clarifying questions, rely on implicit assumptions or infer engineering intent from project context. Every implementation is derived from the information contained in the requirements.

To generate production-ready software, AI needs specifications that describe not only the expected functionality but also the engineering details needed to implement the solution correctly.

Functional Behavior

The requirements should clearly define how the system is expected to behave under all relevant operating conditions.

This includes:

• Inputs
• Outputs
• State changes
• Events and triggering conditions

Without a complete behavioral description, AI cannot determine how the software should react in every situation.

Interfaces

Software rarely operates in isolation. Requirements should describe how the system communicates with external components and services.

Typical interface information includes:

• APIs
• Messages
• Signals
• Communication protocols

Clearly defined interfaces enable AI to generate consistent communication logic and integration code.

Data

Correct software depends on a precise understanding of the information it processes. Requirements should describe both the meaning and the structure of the data.

Important aspects include:

• Data types
• Valid ranges
• Data structures
• Units and formats

Well-defined data models reduce interpretation errors and improve the consistency of the generated implementation.

Constraints

Production-ready software must satisfy more than functional requirements. Engineering constraints often determine whether the implementation is acceptable in its intended environment.

Requirements should specify constraints such as:

• Timing
• Performance
• Safety
• Security

These constraints influence architectural decisions and implementation strategies that AI cannot reliably infer on its own.

Exceptional Situations

Robust software must handle more than the nominal operating scenario. Requirements should explicitly define the expected behavior when abnormal situations occur.

Examples include:

• Invalid inputs
• Communication failures
• Recovery procedures
• Degraded operating modes

Describing exceptional behavior enables AI to generate software that is more resilient and suitable for real-world operation.

Providing this information does not guarantee that AI will generate perfect software, but omitting it almost guarantees that important engineering decisions will be made implicitly.

Missing information does not stop AI. It forces AI to guess. Every guess becomes an engineering decision, and engineering decisions belong in the requirements, not in the generated code.

4. The Biggest Challenges

The vision of generating production-ready software directly from requirements is compelling, but several engineering challenges must be addressed before it can become a reliable development approach. Most of these challenges are not limitations of AI itself; they originate from the quality, completeness and consistency of the engineering specification.

Incomplete Requirements

AI can only implement the information it receives. If essential behaviors, interfaces or constraints are missing from the requirements, they cannot be reflected in the generated software.

Unlike human engineers, AI cannot identify undocumented stakeholder expectations or recover information that was never specified. Missing requirements therefore result in incomplete implementations rather than requests for clarification.

Hidden Assumptions

Engineering teams often rely on implicit knowledge accumulated through previous projects, domain expertise or organizational practices. As a result, important information may be omitted because it is considered “obvious.”

While experienced engineers frequently recognize these assumptions, AI does not share the same project context. Information that exists only in the minds of engineers cannot be transformed into reliable software.

Cross-Document Consistency

Modern engineering projects rarely rely on a single specification. Instead, requirements are distributed across multiple engineering artifacts that evolve throughout the project.

These typically include:

• Thousands of requirements
• Hundreds of interfaces
• System and software architecture documents
• Industry standards
• Safety and security analyses

Generating production-ready software requires AI to understand and maintain consistency across all these sources. Identifying contradictions, resolving conflicts and preserving traceability remain significant engineering challenges.

Design Decisions

Requirements primarily describe what the system shall do. Source code, however, must also define how that behavior is implemented.

Architectural patterns, algorithms, data structures and implementation strategies are often not specified in the requirements. While AI can propose reasonable solutions, many design decisions involve technical trade-offs that depend on project objectives, system constraints and engineering experience.

These decisions continue to require human engineering judgment.

Non-Functional Requirements

Functional behavior alone is rarely sufficient to produce production-ready software. Many of the most critical engineering properties are non-functional and significantly influence the implementation.

Examples include:

• Performance
• Safety
• Timing
• Memory usage
• Concurrency
• Fault tolerance

Unless these expectations are explicitly specified, AI has little basis for selecting an appropriate implementation strategy.

Validation

Even perfectly generated code may implement the wrong solution if the requirements themselves do not reflect stakeholder needs. AI can faithfully transform an incorrect specification into an equally incorrect implementation.

For this reason, code generation does not eliminate the need for Requirements Validation. Engineers must still confirm that the specification represents the intended solution before implementation begins.

The greatest challenge is therefore not teaching AI how to write code. It is providing engineering specifications that are complete, consistent and validated.

5. Can We Trust AI-Generated Code?

Artificial Intelligence is rapidly changing the way software is developed. It can generate functions, classes, APIs, test cases and even complete applications with remarkable speed. However, the ability to generate code should not be confused with the ability to generate the correct code.

The trustworthiness of AI-generated software depends primarily on the quality of the engineering specification from which it is produced. Before AI can generate production-ready code, the requirements themselves must be:

• Correct, accurately reflecting stakeholder needs and intended system behavior.
• Complete, providing all information necessary for implementation.
• Verified, ensuring they are clear, consistent, feasible and testable.
• Validated, confirming they describe the right solution for the intended problem.

Even when generated from high-quality requirements, software cannot be assumed to be correct without verification. Like any implementation, AI-generated code must be verified against its specification to confirm that the required behavior has been implemented completely and correctly. Reviews, static analysis, unit testing, integration testing and system testing remain essential engineering activities. AI may significantly accelerate implementation, but it does not eliminate the need to demonstrate compliance with the requirements before software can be considered production-ready.

When these conditions are met, AI can become a powerful implementation tool, capable of transforming engineering knowledge into high-quality software with unprecedented speed. Conversely, incomplete or ambiguous requirements inevitably lead to incomplete or ambiguous implementations, regardless of whether the code is written by a human or generated by AI.

Artificial Intelligence therefore does not remove the need for engineering. It amplifies it. High-quality requirements enable AI to produce better results, while poor requirements simply allow defects to propagate more quickly through the development process.

The relationship can be summarized simply:

The future of AI-assisted software development will not be defined solely by more capable AI models. It will be defined by the ability of engineers to produce precise, complete and verifiable engineering specifications that AI can implement with confidence.

Production-ready software begins with production-ready requirements, but it is achieved only after the generated implementation has been rigorously verified and the overall solution has been validated. AI accelerates implementation, but the responsibility for delivering correct and reliable software remains firmly in the hands of engineers.

6. Key Takeaways

• AI is transforming software development by shifting the focus from writing code to defining high-quality engineering specifications.
• The quality of AI-generated software depends directly on the quality of the requirements. Better requirements enable better implementations.
• Requirements intended for AI must describe the complete engineering solution, including functional behavior, interfaces, data, constraints, exceptional situations and non-functional requirements.
• Incomplete or ambiguous requirements force AI to make engineering assumptions, increasing the risk of incorrect or inconsistent implementations.
• Generating production-ready code requires production-ready requirements that are correct, complete, verified and validated before implementation begins.
• AI accelerates implementation but does not replace engineering expertise. Engineers remain responsible for defining the right solution, making design decisions where necessary and validating the final system.
• The future of AI-assisted software development depends as much on advances in Requirements Engineering as it does on advances in Artificial Intelligence.

Verification vs Validation: What's the Difference?

Verification and validation are related, but they answer different questions. Although the terms are often used interchangeably, they serve distinct purposes throughout the engineering lifecycle. Verification confirms that an engineering artifact satisfies its specified requirements, while validation confirms that those requirements and the resulting solution fulfill the actual stakeholder needs.

1. Understanding Verification and Validation

Verification and validation are complementary engineering activities that answer two fundamentally different questions:

• Verification: Did we build the product correctly?
• Validation: Did we build the correct product?

Verification evaluates whether an engineering artifact such as requirements, system designs, source code or test cases conforms to its specified requirements. The focus is on correctness and compliance with the defined specification.

Verification at this stage often involves:

• Reviews and inspections
• Static analysis
• Traceability analysis
• Compliance checks
• Verification testing at the unit, integration and system levels

Performing these activities early helps identify defects before they propagate to downstream lifecycle activities, reducing both development effort and project risk.

Validation, on the other hand, determines whether the developed solution fulfills its intended purpose and satisfies stakeholder needs in its operational environment. A technically correct implementation may still fail if it does not address the actual problem or deliver the expected value.

Validation is commonly performed through:

• Stakeholder reviews
• Use case walkthroughs
• Prototypes
• User acceptance testing
• Operational evaluations

These activities provide confidence that the right solution is being developed before significant resources are invested.

Verification and validation are not confined to the testing phase. They should be applied throughout the engineering lifecycle, from capturing stakeholder needs and defining requirements to designing, implementing and validating the final system. Together, they ensure that engineering artifacts are both built correctly and represent the right solution.

2. Verification and Validation Throughout the Engineering Lifecycle

Verification and validation are continuous engineering activities that accompany every stage of product development. Rather than being isolated tasks performed during testing, they provide ongoing confidence that each engineering decision contributes to a successful final product.

Stakeholder Needs

Every engineering project begins with understanding the problem to be solved. At this stage, verification ensures that stakeholder needs have been captured accurately, completely and consistently, providing a reliable foundation for subsequent engineering activities.

Verification at this stage often involves:

• Stakeholder interviews
• Documentation reviews
• Consistency and completeness checks

Validation complements this effort by confirming with stakeholders that the documented needs truly represent their expectations, business objectives and intended use.

Validation at this stage can involve:

• Stakeholder workshops
• Reviews and walkthroughs
• Early concepts or prototypes

Requirements Engineering

As stakeholder needs are transformed into requirements, verification focuses on the quality of the specification. Well-defined requirements reduce ambiguity and provide a solid basis for design, implementation and testing.

Verification checks here include:

• Completeness
• Clarity and unambiguity
• Consistency
• Feasibility
• Testability
• Traceability
• Compliance with writing guidelines

Common techniques for verification include:

• Peer reviews
• Formal inspections
• AI-assisted quality analysis

Validation addresses a different aspect: whether the documented requirements actually describe the intended solution. Even a perfectly written requirement can be invalid if it does not reflect the real stakeholder need.

Validation often takes the form of:

• Stakeholder reviews
• Use case walkthroughs
• Prototypes
• Requirement prioritization and confirmation

System and Software Design

During system and software design, verification ensures that the proposed architecture correctly implements the approved requirements and remains technically consistent.

Verification in this phase includes:

• Architecture reviews
• Interface verification
• Design consistency checks
• Traceability verification
• Compliance with architectural guidelines

Validation evaluates whether the selected architecture is appropriate for the intended product and capable of meeting functional and non-functional expectations.

Validation in this phase may involve:

• Architecture prototypes
• Performance simulations
• Feasibility studies
• Design walkthroughs with stakeholders

Implementation

Implementation transforms the approved design into executable software. Verification provides confidence that the implementation faithfully realizes the design while maintaining quality and consistency.

Verification commonly includes:

• Code reviews
• Static code analysis
• Coding guideline compliance
• Unit testing

Validation focuses on the behavior of the implemented functionality once integrated into the overall system, ensuring that it supports the intended operational scenarios and user expectations.

Validation commonly includes:

• Functional demonstrations
• Integration evaluations
• User feedback during development
• Prototype assessments

Testing

Testing is often associated with verification and validation, but both activities continue to serve distinct purposes.

Verification demonstrates that the implemented system satisfies the specified requirements and that testing itself provides reliable evidence.

Verification typically relies on:

• Test case reviews
• Requirement coverage analysis
• Verification of expected results
• Test environment verification
• Unit, integration, system and regression testing

Validation confirms that the completed product fulfills stakeholder expectations in its intended operational environment.

Validation typically relies on:

• User acceptance testing
• Customer evaluations
• Field trials
• Operational validation

3. Why Both Activities Matter

Delivering successful products requires more than technical excellence. It requires confidence that the product fulfills its intended purpose. Verification and validation work together to build that confidence by addressing different aspects of project success.

Verification helps improve engineering quality by identifying defects early, ensuring consistency across engineering artifacts, and preventing issues from propagating through subsequent lifecycle activities. Early detection reduces rework, shortens development cycles, and lowers the overall cost of quality.

Validation ensures that engineering efforts remain aligned with stakeholder expectations and business objectives. By confirming that the proposed solution addresses the right problem, it reduces the risk of costly redesigns, unnecessary functionality, and products that fail to meet user needs.

Together, verification and validation create a continuous feedback loop throughout the engineering lifecycle. Verification confirms the quality and consistency of engineering outputs, while validation ensures those outputs continue to support the intended goals. Applied together, they improve product quality, reduce technical and business risk, and increase confidence in the final solution.

4. Key Takeaways

• Verification confirms that engineering artifacts conform to their specifications.
• Validation confirms that the developed solution satisfies stakeholder needs and intended use.
• Verification and validation should be applied throughout the engineering lifecycle, not only during testing.
• Combining both activities helps reduce technical defects, minimize project risk and increase confidence in the final product.

AI-Assisted Engineering: Opportunities and Risks

Artificial Intelligence is transforming engineering by enabling faster analysis, stronger consistency and more effective automation. It creates major opportunities to improve productivity, quality and decision support, but it also introduces risks that require responsible engineering practices, human oversight and clear governance.

1. The Rise of AI-Assisted Engineering

Engineering has always evolved alongside technological innovation. From computer-aided design and simulation tools to model-based development and digital twins, each advancement has enabled engineers to manage increasing complexity while improving quality and efficiency. Today, Artificial Intelligence represents the next major step in this evolution.

Modern engineering projects are significantly more demanding than those of the past. Products have become increasingly software-driven, interconnected and data-intensive, while organizations are expected to deliver innovative solutions within shorter development cycles and under stricter quality and regulatory requirements.

Engineers today face challenges such as:

• Increasing system complexity
• Shorter development and release cycles
• Growing volumes of engineering data and documentation
• Knowledge-intensive development across multiple engineering disciplines
• Digital transformation of engineering processes and tools

Managing these challenges requires engineers to process vast amounts of information, maintain consistency across numerous engineering artifacts and make informed decisions within increasingly constrained schedules. Traditional engineering practices remain essential, but the scale and complexity of modern projects demand additional capabilities.

Artificial Intelligence provides those capabilities by enabling computers to analyze information, recognize patterns and generate recommendations based on large collections of engineering knowledge and data. Unlike conventional software, which follows explicitly programmed rules, modern AI systems learn statistical relationships from existing information and use that knowledge to assist with new tasks. This allows AI to understand natural language, summarize documents, identify inconsistencies and support engineering activities that previously required significant manual effort.

Within this engineering context, AI has become an increasingly valuable assistant rather than simply another software tool. It helps engineers analyze large specifications, retrieve relevant knowledge, automate repetitive activities and identify issues that might otherwise remain unnoticed. By reducing the effort spent on routine analysis and documentation, AI enables engineers to dedicate more time to activities that require technical expertise, critical thinking and collaboration with stakeholders.

AI is not changing the purpose of engineering. It is changing how engineers work, providing intelligent assistance that improves productivity, enhances quality and enables organizations to address the growing complexity of modern engineering projects.

2. Opportunities of AI-Assisted Engineering

Artificial Intelligence creates value across the engineering lifecycle by augmenting the capabilities of engineering teams rather than replacing them. By automating routine activities, analyzing large amounts of information and providing intelligent recommendations, AI enables engineers to work more efficiently while maintaining high quality standards.

The greatest opportunities can be grouped into five key areas.

Increased Productivity

Many engineering activities involve repetitive and time-consuming tasks that require significant manual effort. AI can automate these activities, allowing engineers to focus on solving complex technical problems and collaborating with stakeholders.

AI can improve productivity through:

• Automating repetitive engineering tasks
• Accelerating document reviews
• Retrieving relevant information within seconds
• Generating first drafts of engineering artifacts

The result is shorter development cycles, reduced manual effort and more time available for engineering decision-making.

Improved Quality

Maintaining quality becomes increasingly difficult as engineering artifacts grow in size and complexity. AI can continuously analyze specifications and identify issues that may be difficult to detect through manual reviews alone.

AI contributes to higher quality by supporting:

• Consistency checking across engineering artifacts
• Early detection of ambiguities and defects
• Traceability analysis
• Compliance with engineering standards and organizational guidelines

By identifying quality issues early, AI helps reduce rework and increases confidence in engineering deliverables.

Better Decision Support

Engineering decisions often require evaluating large amounts of information distributed across multiple documents, projects and disciplines. AI assists engineers by rapidly analyzing this information and presenting relevant insights.

Typical decision-support capabilities include:

• Pattern recognition across specifications
• Retrieval of relevant engineering knowledge
• Change impact analysis
• Generation of recommendations and alternative approaches

Rather than making decisions, AI provides engineers with better information on which to base their engineering judgment.

Knowledge Preservation

Engineering organizations accumulate valuable knowledge through years of projects, reviews and technical decisions. Much of this knowledge remains difficult to access or is lost when experienced engineers change roles or leave the organization.

AI helps preserve and reuse engineering knowledge through:

• Access to organizational knowledge repositories
• Learning from previous projects and specifications
• Faster onboarding of new engineers
• Semantic search across engineering documentation

Making organizational knowledge easier to discover reduces duplicated effort and encourages the reuse of proven engineering solutions.

Innovation

Beyond improving efficiency, AI can also stimulate creativity by helping engineers explore new ideas and evaluate different solution approaches.

AI can support innovation through:

• Alternative solution proposals
• Brainstorming and idea generation
• Architecture exploration
• Scenario generation and evaluation

While innovation ultimately depends on human creativity and engineering expertise, AI provides valuable inspiration and accelerates the exploration of possible solutions.

When applied responsibly, AI enables engineering teams to become more productive, deliver higher-quality results, make better-informed decisions and capitalize on organizational knowledge. Its greatest value lies not in replacing engineers, but in empowering them to focus on the activities where human expertise delivers the greatest impact.

3. Risks of AI-Assisted Engineering

Artificial Intelligence offers significant opportunities for engineering organizations, but like any engineering tool, it also introduces new challenges that must be understood and managed. Responsible adoption requires recognizing both the capabilities and the limitations of AI, ensuring that engineering decisions remain based on sound technical judgment.

The following areas represent the most important risks when integrating AI into engineering activities.

Incorrect Results

AI generates responses by identifying patterns in existing data rather than by reasoning like a human engineer. As a result, it may produce outputs that appear convincing but are technically incorrect, incomplete or inappropriate for the engineering context.

Potential risks include:

• Hallucinations or fabricated information
• Incorrect assumptions
• Missing project or domain context

AI-generated outputs should therefore be treated as recommendations that require engineering review and validation before being incorporated into engineering artifacts.

Overreliance

As AI systems become more capable, there is a risk that engineers place excessive trust in their recommendations. Accepting AI-generated results without sufficient review can reduce the effectiveness of established engineering practices.

Common risks include:

• Blind acceptance of AI-generated content
• Reduced critical thinking
• Automation bias, where AI recommendations are assumed to be correct

Engineering expertise, independent reviews and professional judgment remain essential to ensure the quality and correctness of engineering decisions.

Data Quality

The quality of AI-generated results depends directly on the quality and relevance of the information available to the AI system. Incomplete, outdated or inaccurate knowledge can lead to misleading recommendations and poor engineering decisions.

Potential limitations include:

• Incomplete engineering knowledge
• Outdated standards or specifications
• Poor-quality training or reference data

Maintaining reliable engineering knowledge and regularly updating AI knowledge sources are essential for achieving trustworthy results.

Confidentiality

Engineering projects often involve sensitive information that must be protected. When using AI services, organizations must ensure that confidential data is handled in accordance with contractual, legal and organizational requirements.

Areas requiring particular attention include:

• Intellectual property
• Customer and project data
• Export-controlled or restricted information

Appropriate governance, secure AI platforms and clear organizational policies are necessary to protect confidential engineering information.

Compliance

Many engineering domains, particularly safety-critical industries, require strict compliance with standards, regulations and documented engineering processes. AI-assisted engineering must support these obligations rather than compromise them.

Important considerations include:

• Maintaining traceability across engineering artifacts
• Providing explainable and reviewable outputs
• Supporting compliance with safety and quality standards
• Meeting regulatory and customer expectations

Organizations should ensure that AI-generated artifacts remain transparent, reviewable and fully integrated into their established engineering and quality management processes.

4. Responsible AI Adoption

Successfully integrating Artificial Intelligence into engineering processes requires more than selecting the right tools. Organizations must establish clear principles that ensure AI enhances engineering activities while preserving quality, accountability and compliance. The goal is not to replace established engineering practices, but to strengthen them with intelligent assistance.

The following principles provide a foundation for the responsible adoption of AI in engineering.

Keep Engineers in Control

AI should support engineering activities, not replace engineering responsibility. Engineers remain accountable for technical decisions, stakeholder communication and the approval of engineering artifacts.

Good practices include:

• Treat AI outputs as recommendations rather than authoritative results.
• Review and validate AI-generated content before use.
• Maintain human approval for engineering decisions.
• Clearly define responsibilities for AI-assisted activities.

Integrate AI into Established Engineering Processes

AI delivers the greatest value when embedded within existing engineering workflows rather than used as a separate or isolated activity.

Organizations should:

• Integrate AI into existing engineering methods and review processes.
• Preserve established quality assurance activities.
• Maintain traceability between AI-assisted outputs and engineering artifacts.
• Continuously evaluate the effectiveness of AI-supported processes.

Ensure Transparency and Traceability

Engineering decisions must remain understandable and reproducible, particularly in regulated and safety-critical environments.

Responsible adoption includes:

• Recording significant AI-assisted engineering activities where appropriate.
• Maintaining traceability between inputs, outputs and engineering decisions.
• Ensuring AI-generated artifacts remain reviewable and explainable.
• Preserving version control and configuration management.

Protect Engineering Information

Engineering data often represents valuable intellectual property and may contain confidential customer or project information. AI must be deployed in a manner that protects these assets.

Organizations should:

• Apply appropriate data protection policies.
• Use trusted AI platforms and services.
• Protect confidential and export-controlled information.
• Define clear guidelines for the use of external AI services.

Continuously Improve AI Usage

AI capabilities evolve rapidly, requiring organizations to regularly evaluate both the technology and the way it is used within engineering processes.

Continuous improvement includes:

• Monitoring the quality of AI-assisted outputs.
• Updating engineering knowledge sources.
• Training engineers on AI capabilities and limitations.
• Refining prompts, workflows and governance based on project experience.

Build Trust Through Responsible Use

Successful AI adoption depends on trust. Engineers must understand when AI can provide reliable assistance, when additional review is required and where engineering judgment remains essential.

Organizations should promote:

• Critical evaluation of AI-generated results.
• Awareness of AI limitations and potential biases.
• Responsible and ethical use of AI.
• A culture where AI complements, rather than replaces, engineering expertise.

5. Key Takeaways

• AI is transforming engineering by automating repetitive activities, accelerating analysis and providing intelligent decision support.
• AI creates opportunities to improve productivity, engineering quality, knowledge management, decision-making and innovation across the engineering lifecycle.
• AI also introduces risks, including incorrect results, automation bias, data quality issues, confidentiality concerns and regulatory challenges that must be actively managed.
• Engineers remain responsible for reviewing AI-generated outputs, making engineering decisions and approving the final engineering artifacts.
• Responsible AI adoption requires clear governance, human oversight, secure handling of engineering information and integration into established engineering processes.
• The greatest benefits are achieved when AI and engineers work together, combining the analytical capabilities of AI with human expertise, creativity and engineering judgment.

Writing Better Requirements: Best Practices and Common Pitfalls

High-quality requirements are the foundation of successful engineering projects. Learn the principles of writing clear, consistent and verifiable requirements, discover common pitfalls to avoid and explore practical techniques for improving requirement quality.

1. Why Requirement Quality Matters

Requirements are the foundation of every successful engineering project. They translate stakeholder needs into clear, actionable specifications that guide the development of systems, software and products throughout the engineering lifecycle. Every subsequent activity from system design and implementation to verification and validation depends on the quality of the requirements.

Requirements also serve as the primary means of communication between all project stakeholders. They establish a shared understanding of the expected system behavior and provide a common reference for technical and business decisions throughout the project.

Requirements enable effective collaboration between:

• Stakeholders and customers
• Requirements Engineers
• System architects
• Software developers
• Verification and validation engineers
• Project and quality managers

When requirements are clear, complete and unambiguous, engineering teams can work with confidence, reducing misunderstandings and improving consistency across the entire development process.

Poorly written requirements, however, introduce uncertainty that propagates throughout the engineering lifecycle. Ambiguous or incomplete requirements often lead to different interpretations, resulting in incorrect implementations, ineffective testing and costly redesign efforts.

The consequences of poor requirement quality may include:

• Misunderstandings between stakeholders and engineering teams
• Increased rework due to incorrect implementation
• Higher development and maintenance costs
• Incomplete or ineffective verification and validation
• Project delays caused by requirement clarification and redesign
• Increased safety, security and regulatory compliance risks

Because correcting requirement defects becomes significantly more expensive as development progresses, investing in high-quality requirements from the outset is one of the most effective ways to reduce project risk and improve engineering efficiency.

Well-written requirements are more than documentation, they are the foundation for effective communication, informed engineering decisions and successful product development.

2. Characteristics of a Good Requirement

A well-written requirement is more than a statement describing expected system behavior. It is a precise engineering artifact that enables effective communication, supports design and implementation, and provides a reliable basis for verification and validation. High-quality requirements share a set of characteristics that reduce ambiguity, improve consistency and minimize project risk.

Unambiguous

A requirement should have only one possible interpretation. Every stakeholder, engineer and tester should understand the requirement in exactly the same way. Ambiguous wording can lead to different implementations, inconsistent testing and costly misunderstandings. Using precise terminology, measurable criteria and well-defined concepts helps eliminate uncertainty.

Complete

A requirement should contain all the information necessary to understand and implement the expected behavior. Missing conditions, constraints or acceptance criteria often force engineers to make assumptions, increasing the risk of incorrect implementations. A complete requirement minimizes the need for clarification during development.

Atomic

Each requirement should describe a single behavior, function or constraint. Combining multiple expectations into one statement makes the requirement more difficult to understand, implement and verify. Atomic requirements simplify traceability, change management and testing by ensuring that each requirement can be evaluated independently.

Consistent

A requirement should not contradict other requirements or established engineering decisions. Inconsistencies within a specification create confusion, increase implementation effort and may result in conflicting system behavior. Maintaining consistency across the specification ensures that all engineering artifacts describe the same intended solution.

Feasible

A requirement should be technically achievable within the project’s constraints, including available technology, budget, schedule and regulatory requirements. Defining unrealistic or impractical requirements introduces unnecessary project risk and may lead to costly redesign efforts later in the development lifecycle.

Necessary

Every requirement should provide clear value by addressing a stakeholder need, business objective or regulatory obligation. Unnecessary requirements increase system complexity, development effort and maintenance costs without delivering meaningful benefits. Keeping only essential requirements helps maintain a focused and manageable specification.

Verifiable

A requirement should define expected behavior in a way that can be objectively confirmed through inspection, analysis, demonstration or testing. Requirements containing subjective expressions such as fast, user-friendly or efficient cannot be verified consistently. Clear acceptance criteria and measurable constraints provide confidence that the requirement has been correctly implemented.

Traceable

A requirement should maintain clear relationships with the engineering artifacts that justify, implement and verify it. Traceability enables engineers to understand why a requirement exists, assess the impact of proposed changes and demonstrate complete coverage from stakeholder needs through design, implementation and testing. Effective traceability is particularly important in large-scale and safety-critical engineering projects.

Together, these characteristics define the quality of a requirement. The more consistently they are applied, the easier it becomes to communicate, implement, verify and maintain engineering specifications throughout the development lifecycle.

3. A Recommended Requirement Structure

A consistent sentence structure improves the clarity, readability and quality of requirements. When requirements follow a common syntax, they become easier to understand, review, implement and verify. A structured format also promotes consistency across the specification and reduces the likelihood of ambiguous or incomplete statements.

The following pattern provides a practical guideline for writing both functional and non-functional requirements. The elements shown in parentheses are optional and should be included only when they contribute to a clearer understanding of the requirement.

The core of every requirement is formed by the sequence Subject → Obligation → Action. Depending on the engineering context, optional elements such as Object, Condition, Target and Constraint can be added to define when the requirement applies, who or what is affected by the action, and any measurable criteria that must be satisfied.

This example includes all elements of the recommended syntax pattern:

• Condition: When the ignition is switched on
• Subject: The Body Control Module
• Obligation: shall
• Action: transmit
• Object: the diagnostic response
• Target: to the instrument cluster
• Constraint: within 100 ms

Not every requirement contains all optional elements. The following requirement illustrates a simpler structure.

In this case, the action describes a property of the subject itself rather than acting upon an object. The requirement therefore consists of the core elements and a target:

• Subject: The communication interface
• Obligation: shall
• Action: comply with
• Target: the CAN FD specification

The recommended syntax is intended as a practical guideline rather than a rigid grammar. The goal is to write requirements that are clear, consistent and easily understood, while including only the information necessary to describe the expected behavior accurately.

4. Best Practices for Writing Requirements

Writing high-quality requirements requires more than following a predefined syntax. Effective requirements communicate expected behavior clearly, minimize ambiguity and provide a reliable basis for implementation and verification. The following best practices help engineers produce requirements that are easier to understand, review and maintain throughout the engineering lifecycle.

Use Clear and Precise Language

Requirements should be written using simple, precise and objective language. Every reader should interpret the requirement in the same way, regardless of their role or experience. Avoid subjective or vague expressions that cannot be measured or verified.

Describe One Requirement at a Time

Each requirement should describe a single behavior, function or constraint. Keeping requirements atomic simplifies implementation, testing, traceability and change management.

State What, Not How

Requirements should define the expected behavior without unnecessarily prescribing the implementation. Leaving implementation decisions to system and software design provides greater flexibility and encourages better technical solutions.

Define Measurable Acceptance Criteria

Whenever possible, requirements should include measurable constraints that allow objective verification. Time, performance, accuracy and capacity are common examples of measurable criteria.

Examples include:

• Response time
• Accuracy
• Tolerance
• Throughput
• Capacity
• Availability

Measurable requirements eliminate subjective interpretation and make verification straightforward.

Specify Conditions When Relevant

Many system behaviors apply only under specific circumstances. Clearly defining the triggering condition helps engineers understand when the requirement becomes applicable and prevents incorrect assumptions.

Use Consistent Terminology

The same concept should always be described using the same terminology throughout the specification. Using multiple names for the same component, signal or function increases the likelihood of misunderstandings and inconsistencies.

Whenever possible:

• Use defined glossary terms.
• Follow established naming conventions.
• Reuse terminology consistently across all engineering artifacts.

Write Requirements That Can Be Verified

Every requirement should be written so that compliance can be demonstrated through inspection, analysis, demonstration or testing. If there is no objective way to verify a requirement, it should be refined before becoming part of the specification.

A useful question to ask is:

How will we prove that this requirement has been satisfied?

If the answer is unclear, the requirement likely needs further refinement.

Applying these best practices consistently improves requirement quality, simplifies collaboration between engineering disciplines and reduces the risk of misunderstandings throughout the development lifecycle. Well-written requirements provide a solid foundation for successful system development and effective verification and validation.

5. Common Pitfalls

Even well-structured specifications may contain defects that reduce clarity, introduce ambiguity or complicate implementation and verification. The following pitfalls are among the most common causes of poor requirement quality.

Ambiguous Wording

Ambiguous wording occurs when a requirement contains subjective or imprecise expressions that allow multiple interpretations. Terms such as quickly, easily, normally, sufficient or robust do not have a universally accepted meaning and often lead to inconsistent implementation and verification.

Multiple Behaviors

A requirement that describes multiple behaviors or obligations in a single statement becomes more difficult to understand, implement, verify and maintain. Such requirements frequently contain conjunctions such as and, or or also, making independent verification and traceability more challenging.

Passive Voice

Passive voice obscures the system element responsible for performing the required action. When the actor is not explicitly identified, responsibilities become less clear and different engineering teams may interpret the requirement differently.

Missing Conditions

Requirements that omit the conditions under which a behavior applies often leave engineers to assume when the functionality should be executed. This may result in inconsistent implementations and incomplete test coverage.

Missing Actor

A requirement without an explicitly identified subject does not clearly specify which system, subsystem or component is responsible for performing the required behavior. This ambiguity can lead to inconsistent design decisions and implementation responsibilities.

Missing Units or Measurable Criteria

Numerical values without units or qualitative statements without measurable acceptance criteria cannot be objectively verified. Such requirements often rely on subjective interpretation rather than measurable engineering evidence.

Negative Requirements

Negative requirements describe what the system shall not do rather than defining the expected behavior. While appropriate in some situations, they frequently provide insufficient guidance for implementation and make objective verification more difficult.

Implementation Bias

Implementation bias occurs when a requirement prescribes a specific technical solution instead of describing the required behavior. Premature implementation decisions unnecessarily restrict architectural and design choices and reduce the flexibility to select the most appropriate solution.

These pitfalls frequently occur in engineering specifications and are a common source of misunderstandings, rework and verification issues. Identifying and correcting them early improves specification quality and reduces project risk throughout the engineering lifecycle.

6. Key Takeaways

• Requirements are the foundation of successful engineering projects, providing a common understanding between stakeholders and engineering teams.
• High-quality requirements are unambiguous, complete, atomic, consistent, feasible, necessary, verifiable and traceable, enabling reliable implementation and objective verification.
• A consistent requirement structure improves readability, reduces ambiguity and promotes consistency across the entire specification.
• Clear, measurable and implementation-independent requirements are easier to understand, implement, maintain and test.
• Applying proven writing practices helps reduce misunderstandings, minimize rework and improve collaboration across engineering disciplines.
• Recognizing common pitfalls during requirement reviews helps identify defects early, improving specification quality before development begins.
• Investing in high-quality requirements early reduces project risk, lowers development costs and provides a stronger foundation for successful system development.