Assumption Zero: Human Requirement Verification

Modern software can pass every automated test and still fail the people it was designed to serve. Many systems silently assume that every user has a surname, permanent address, conventional phone number, reliable internet connection, or other standardized identity and access conditions. These assumptions are often distributed across multiple layers of an application, including the frontend, APIs, backend validation, database schema, automated tests, and documentation. Because each individual component may appear technically correct, these human-facing failures can remain invisible until a real user is blocked. Assumption Zero is a human requirement verification system designed to uncover these hidden assumptions before they reach production. The platform analyzes both software requirements and the repository itself. Specialized analysis agents inspect the frontend, backend, APIs, database, tests, and resilience logic in parallel. Assumption Zero then maps requirements to concrete implementation evidence and constructs an Assumption Graph showing where a human requirement is supported, contradicted, or missing across the software stack. Instead of only reporting code smells, Assumption Zero converts detected assumptions into realistic counterexample human journeys. For example, our demonstration follows Noor, a user with one legal name, no permanent address, an international Pakistani phone number, and potentially unreliable connectivity. Although the application may appear functional for conventional test data, Noor's journey exposes failures caused by mandatory surname fields, address requirements, restrictive phone validation, or assumptions about continuous network access. The system traces each failure back to the relevant code and requirement, presents supporting evidence, and proposes coordinated repairs across affected layers. Developers can review the proposed changes, apply fixes, and run verification again to determine whether the original user journey now succeeds without creating regressions elsewhere. The target users are software engineering teams building systems where human accessibility and inclusivity are critical, particularly public-service, nonprofit, healthcare, education, emergency-response, and accessibility-focused applications. What makes Assumption Zero different is its focus on the gap between technical correctness and human correctness. Traditional testing asks whether software behaves according to predefined test cases. Assumption Zero asks a deeper question: What assumptions did we make about the human using the system? By combining repository-scale analysis, requirement-to-code evidence mapping, cross-layer reasoning, counterexample journey generation, repair planning, and regression verification, Assumption Zero turns hidden assumptions into visible, testable engineering problems. The result is software that is not only technically functional, but more resilient to the diversity of real people and real-world circumstances.