How to verify AI-written mobile code before you ship — Articles

AI-generated mobile code should be verified against explicit user outcomes and the exact build intended for release rather than relying on agent summaries, screenshots, or mocked tests. The process includes defining acceptance criteria, recording commit, dependency, native-build, and update identifiers, reviewing diffs and tests for weakened assertions or untested assumptions, and exercising installed iOS and Android apps through realistic success, failure, upgrade, permission, session, and recovery scenarios. Verification should confirm server-side persistence and authorization, examine security risks such as bundled secrets, logging, cached account data, and access controls, and use frameworks such as OWASP MASVS for structured mobile-security review. Release decisions should document the tested build, evidence, failures, accepted gaps, rollout and rollback implications, and post-release monitoring, while follow-up changes should trigger reruns based on their effects on UI, dependencies, native code, authentication, or sensitive workflows. Expo tools including EAS Build, Workflows, fingerprints, MCP Server, simulators, and Observe can help connect testing and approvals to a specific build, but they do not replace human-defined assertions, real-device testing, or security review.

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