Food Delivery App Testing Checklist (2026)

Food delivery is real-time, location-critical, time-sensitive, and money-handling. Each of these creates its own failure modes. Testing well means covering browse, cart, checkout, rider tracking, and

Food delivery is real-time, location-critical, time-sensitive, and money-handling. Each of these creates its own failure modes. Testing well means covering browse, cart, checkout, rider tracking, and post-order support — plus the edge cases unique to perishable goods and time-of-day pricing.

Browse and discovery

1. Location permission prompt at the right moment
2. Manual address entry as fallback
3. Nearby restaurants load within 3 seconds
4. Restaurant status (open/closed/busy) accurate and updates
5. Menu categories navigable, items cached per restaurant
6. Out-of-stock items visible and not selectable
7. Dietary filters (veg, vegan, gluten-free) apply consistently
8. Search with typos still returns useful results
9. Cuisines, ratings, price ranges filter as expected
10. Promoted/sponsored placement clearly labeled

Cart and checkout

1. Item customizations (size, add-ons, spice level) saved per item
2. Special instructions field included with order
3. Cart total includes taxes, service fees, delivery fee, tips transparent
4. Promo code validates against rules (new user, first order, specific restaurant)
5. Multiple restaurants — separate carts (or merged with clarity)
6. Cart persists across app kill
7. Schedule-for-later slot picker functional and respects restaurant hours
8. Checkout button locked after first tap
9. Payment methods — saved cards, wallets, COD where applicable
10. OTP / 3DS / bank redirect flows complete

Order tracking

1. Order confirmation shows estimated time, order ID
2. Live tracking updates every 5-15 seconds
3. Rider location accurate within reasonable bounds
4. ETA updates reflect traffic, pickup time
5. Stage transitions clear (confirmed → preparing → picked up → delivering → delivered)
6. Push notification fires at each stage
7. In-app rider contact (call, chat) functional
8. Rider name, photo, vehicle info shown

Post-order

1. Rate order, rate rider, rate restaurant — each separately
2. Tip after delivery if not tipped earlier
3. Order history persists
4. Receipt downloadable / emailable
5. Re-order button functional
6. Issue / complaint flow accessible from order details
7. Refund status visible when requested

Edge cases

1. GPS degraded — manual address still works
2. Restaurant cancels mid-order — refund process automatic
3. Rider unavailable — reassignment transparent
4. Address entered wrong — edit before confirmation, support contact after
5. Order during peak (New Year's Eve, Diwali) — app remains responsive
6. Very slow kitchen — ETA updates, user offered action
7. Two devices logged in — last-action-wins or sync
8. Background the app during rider tracking — re-opens to current state
9. Rider arrives at wrong building — chat support accessible
10. Partial order (one item missing) — partial refund flow

Payment-specific

1. UPI / wallet / card — all tested in sandbox + production
2. Duplicate charge prevention on retry
3. Refund turnaround time clear
4. Payment failure — cart preserved, retry possible
5. Cash on delivery — change request captured

Performance

1. Menu load time under 2 seconds
2. Scrolling menus smooth on mid-tier devices
3. Image-heavy screens do not OOM
4. Rider location map does not drain battery

Accessibility

1. Screen reader reads item names, prices, customizations
2. Touch targets ≥ 48dp across menu, cart, checkout
3. Contrast meets WCAG AA including promotional banners
4. Voice-controlled ordering (if supported) handles accents

Security

1. Card details never logged
2. PII (name, phone, address) only in authenticated endpoints
3. Delivery address visible only to assigned rider, not browseable
4. Rider chat messages encrypted in transit
5. Order history for different account not accessible via IDOR

How SUSA tests food delivery

Personas map well: impatient catches slow screens (menu, cart, ETA); novice catches first-time flow confusion; adversarial stresses checkout (rapid taps, invalid promo codes, concurrent orders); elderly checks readability of menus and order confirmation. SUSA's flow tracking recognizes checkout flows automatically and logs verdicts for the full path. Network intelligence flags slow endpoints (menu API, rider GPS refresh) that degrade UX without crashing the app.

susatest-agent test foodapp.apk --persona impatient --steps 150
susatest-agent test foodapp.apk --persona adversarial --steps 150

Food delivery apps have a high bug surface area per release. Run SUSA every build. Manual-test every end-to-end flow weekly. Keep the regression suite generated from SUSA runs up to date — it catches the flaky rider-tracking regression every time.