Requirements engineering traps​

Despite its value, requirements engineering comes with challenges that must be actively addressed:

• The Illusion of completeness from the start: Attempting to define all requirements upfront is unrealistic. Requirements evolve as understanding of the system deepens. If all requirements were known from the beginning, requirements engineering wouldn’t be needed in the first place.
• Overemphasis on categorization: Theoretical distinctions like functional vs. non-functional or quality requirements can create confusion. In practice, the most important thing is not how a requirement is categorized, but that it is documented, understood, and used effectively in development.
• Testing only against requirements: While systems are often tested against their documented requirements, this approach can fall short. If the original requirements fail to fully capture customer needs or ignores physical principles, passing the tests may not guarantee customer satisfaction. Validating requirements early is just as crucial as testing against them later.
• The decomposition trap: Decomposing high-level requirements too early into subsystem or component requirements can waste time if done prematurely or without sufficient context. In uncertain situations, it’s often better to proceed with development activities and revisit decomposition later.
• Resistance to change: Requirements are often treated as fixed, yet they must evolve in response to new insights, changing constraints, or better system understanding. Flexibility is key – embracing change rather than resisting it ensures that the system remains aligned with its goals.

• Cognitive bias during elicitation: Requirements are gathered from people, and people come with built-in cognitive biases. Stakeholders may unintentionally skew what they share based on recent frustrations (recency bias), overvalue the first solution mentioned (anchoring bias), or filter information that confirms their beliefs (confirmation bias). These subconscious effects can lead to incomplete or distorted requirements, even when the process appears thorough.
• Tacit knowledge is left undocumented: Many critical requirements never get mentioned because stakeholders assume they are obvious or “go without saying.” Especially in experienced teams or domain-heavy projects, essential knowledge remains in people’s heads, not in the requirement set. This tacit knowledge trap leads to gaps that only surface later—often during implementation or testing.
• Internal politics shape requirements: Requirements are not always purely technical or customer-driven. Sometimes they reflect the internal power structures of an organization. A department may push certain features to justify budget or influence, or a high-ranking manager might override practical needs with personal preferences. These politically motivated requirements may misalign with real system goals.
• Tool obsession leads to “requirement theater”: With modern requirements tools offering rich feature sets—like traceability, auto-linking, and dashboards – teams may mistake polished documentation for solid understanding. The danger is focusing too much on managing tools and templates, and not enough on the actual substance of the requirements. The process looks good on paper but lacks depth.
• Cultural and language mismatches in global teams: In international projects, seemingly simple terms can mean different things across cultures. Phrases like “easy to use” or “sufficient performance” may carry varied interpretations, depending on local expectations or linguistic nuance. Cultural norms around hierarchy and communication can also suppress valuable feedback or disagreement.
• Designing for personas, not real users: Personas are often used to represent typical users, but if they’re based on assumptions rather than real user data, they can lead to misleading requirements. Designing for a fictional character may feel structured and user-centric, but can result in solutions that miss the mark for actual end users.
• Requirements inflation due to regulation or AI ethics: Especially in AI systems or regulated industries, requirements lists grow long with broad, abstract concepts like fairness, explainability, or accountability. These requirements are hard to validate, difficult to implement, and often added “just in case” to check compliance boxes – leading to bloated requirement sets with unclear value.