Logistics6 months engagement

Freight Management SaaS

QA for a cloud-based freight management platform used by trucking companies to manage dispatching, invoicing, driver tracking, and compliance documentation. Small and mid-size carriers relied on the platform to run their daily operations, from assigning loads to generating BOL documents to collecting payment. QA was critical because invoicing errors could mean trucking companies under-billed their customers, and compliance documentation mistakes could result in DOT fines or trucks being taken out of service.

WebAndroidiOSAPIMobile TestingFunctional QAPerformance Testing

100%

Invoice accuracy

500+

Vehicles tested

Critical bugs caught

The Challenge

Real-Time GPS at Scale

Hundreds of vehicles tracked simultaneously with location updates every 30 seconds, all displayed on the dispatch map without lag.

Complex Invoicing Logic

Rate structures with fuel surcharges, detention fees, accessorial charges, and multi-stop billing had to be legally accurate.

Low-End Device Support

The driver app had to work reliably on budget Android devices with limited memory and intermittent cellular in rural areas.

DOT Compliance Risk

Inaccurate Hours of Service logs or inspection reports could result in DOT fines or trucks being taken out of service.

Tools Used

Appium

Playwright

Postman

AWS

JIRA

Appium

Playwright

Postman

AWS

JIRA

Our Approach

How We Tested This Project

Invoice Engine Validation

We built a spreadsheet-based reference calculator that independently computed invoice totals for every rate structure type. We then compared the platform output against our reference for 500+ historical loads and flagged every discrepancy, no matter how small.

Driver App Field Testing

Tested the driver app on a range of low-end Android devices under real-world conditions: limited memory, poor cellular signal, background app kills, and extended usage sessions. We profiled memory and battery usage to identify the leaks causing crashes on budget devices.

GPS and Real-Time Systems

Built a test harness that simulated GPS data streams from 500+ vehicles to validate map rendering performance, geofence trigger accuracy, and the dispatching system response to live location updates. We tested scenarios like signal loss, GPS drift, and rapid location changes during highway driving.

Migration Validation

Wrote automated comparison scripts that validated every migrated load record field-by-field against the legacy system export. We checked load details, invoice amounts, driver assignments, and compliance documents to ensure nothing was lost or corrupted in the migration.

Scope

What We Tested

01Real-time GPS tracking accuracy and map rendering latency with 500+ simultaneous vehicle updates

02Invoice calculation with complex rate structures including fuel surcharges, detention, and multi-stop billing

03Driver app testing on low-end Android devices (2GB RAM) with constrained CPU and memory

04DOT compliance document generation accuracy for Hours of Service, DVIR, and insurance certificates

05Multi-stop route optimization validation against known-optimal reference routes

06Offline mode for drivers in low-signal areas with queued dispatch updates and document uploads

07Data migration validation for 50,000+ historical loads from the legacy system

08Dispatch assignment workflow including driver availability, load matching, and ETA calculations

09Customer portal invoice viewing, payment processing, and dispute submission flows

10Geofence-triggered events for automated arrival and departure timestamps

Impact

Results & Business Impact

100% Invoice Accuracy

15 critical calculation bugs found, including one silently under-billing fuel surcharges by 3% for months.

Zero Migration Issues

200+ carriers migrated from the legacy system with zero data discrepancy tickets.

60% Fewer Crashes

Driver app crash rates dropped 60% after identifying memory leaks on devices with less than 3GB of RAM.

200 → 500+ Vehicles

Platform grew to 500+ tracked vehicles. Dispatch response time to delivery exceptions improved by 8 minutes.

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