Car manufacturers and suppliers are faced with numerous challenges today.

Technological transformation pushes speedy innovation cycles and a short Time-to-Market, especially in the area of software development for vehicles. Our QualityMinds create intelligent networks and end-to-end test automation which saves time and costs.

Global Customer Data Management

A well-known German car manufacturer needs global customer data management system as a central middleware platform for all its customers. It includes online presence, mobile apps, internal CRM and SAP systems and many other interfaces. The rollout takes place in 80 countries.

Automotive Case study #1

Our contribution

QualityMinds provides consulting services, in particular concerning agile testing strategy and test automation. With the help of dedicated agile test experts in the project of 70 employees and five scrum teams, we implement those changes in an agile manner. Within four years, the high test complexity of the platform with numerous APIs has lead to a total, end-to-end automation of tests, including load and performance tests, as well as defect management.


Efficient tests for control software

A well-known German car manufacturer is looking for a solution for a time and cost-efficient rollout of the vehicle control device software. The tests required for this process are selected manually, while planning and implementation steps are carried out automatically with the help of large teams. The customer wants an innovative automated method of selecting and planning tests. At the same time, the planning process is to be continuously improved.

Automotive Case study #2

Our contribution

First of all, QualityMinds invite the customer to a “discovery workshop”, during which we aim for a precise description of the issue. Within just four months a first draft version of the product is created with the help of kanban and usability studies. The selection of relevant tests is automated through machine learning – the AI automatically ​​suggests and plans required tests across the departments, based on the underlying data. It massively accelerates the process and the AI learns with every iteration.


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