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The Specification Paradox: Rethinking Requirements Engineering in the Age of AI

Citation. Sirqueira, T. & Faciroli, J. (2026). The Specification Paradox: Rethinking Requirements Engineering in the Age of AI. arXiv:2608.16618.

Summary. This conceptual preprint argues that generative AI can reduce implementation effort in software development while relocating work toward domain understanding, requirements elicitation, specification, validation, maintenance, and evolution. It names this dependence on correct, complete, verifiable, and explainable human-produced specifications the Specification Paradox, and argues that requirements engineering becomes more consequential as AI generates more code and related artifacts.

Key claims

• AI-assisted code generation can reduce accidental implementation complexity without eliminating essential complexity rooted in problem understanding, domain constraints, requirements, and evolving objectives.
• In specification-driven development, ambiguous, incomplete, or inconsistent requirements can be propagated into generated code, tests, and documentation; traceability and human validation are therefore central controls.
• The paper reports a cited systematic review of 39 studies in which most studies found implementation-time or repetitive-task productivity gains, but 59% were exploratory and only 15% assessed more than three SPACE productivity dimensions.
• It reports cited regulatory-compliance experiments in which paragraph-level context increased requirement-analysis accuracy from about 41% to 81% for GPT-4, 30% to 63% for GPT-3.5, and 33% to 69% for Mixtral.

Evidence & limitations

• This is an eight-page arXiv preprint and primarily a conceptual synthesis; its empirical statements are drawn from cited studies rather than a new study by the authors.
• The Drive PDF exactly matches the public arXiv v1 PDF by SHA-256; no model-directed prompt-injection language was found in its extracted text.
• Its mechanisms and examples concern software engineering, so transfer to other occupational settings requires separate evidence.

Feeds

• work-redesign
• critical-thinking
• Proposed new topic: ai-assisted-requirements-engineering — how AI-generated software shifts control from implementation toward specification quality, traceability, and validation.