Munchable builds 450-word test suite to prevent silent gaps in food label scanning

Food app Munchable discovered its ingredient-scanning rules engine was returning 'not assessed' for common condition-relevant ingredients, including kefir for a lactose-intolerant user, soya for an IBS user, and tea for a reflux user. Each failure traced back to a missing alias rather than an absent rule, meaning the engine could not match a label's spelling to the rule it was supposed to trigger. To prevent recurrence, the team created a hand-maintained file of 450 label words grouped by dietary condition, covering FODMAP, lactose, GERD, IBD, and gastroparesis categories. Each word in the list is tested by walking the same canonicalisation and taxonomy path that a real product scan follows, ensuring the promise in the file reflects actual engine behaviour. A separate list of 15 everyday words such as water, salt, and sugar is also checked, to stop the app from flagging harmless ingredients as unassessed and drowning out genuinely important alerts.