Model-based testing workbench
Model-based testing is a methodology and IT toolset designed to accelerate the creation of test scripts. Using traditional methods, test professionals must manually create each test script and use case, a task that requires both testing skills and domain expertise. By using model-based testing tools, you can automatically generate test cases from models to describe the application, object or system under test. A single model can generate multiple test cases in far less time than it would take to write the same number of cases manually. Models can also be re-used to test other applications with similar functionality.
Capgemini has developed model-based testing workbenches for key commercial applications including VisionPLUS® and TSYS® for Cards; SEPA Direct Debits and Credit Transfer for Payments and Global PAYplus® for payments and FLEXCUBE®, SAP® Core Banking, MortgageServ®, Temenos® T24, and Cassiopae® for Banking. Our workbenches use pre-built models to jump-start your testing efforts for common applications.
Full spectrum of testing services
We provide the full spectrum of testing services, from an assessment of your current testing organisation to test automation using our robust reuse library of test scripts, scenarios and use cases specifically developed for banking systems. With over 1200 test professionals experienced in banking and cards systems – within our larger financial services testing practice of more than 5,900 professionals – Capgemini can provide deep domain knowledge of your systems.
We have experience managing global testing services for banks, card acquirers, card issuers and other financial institutions where we’ve helped clients:
- Optimise quality assurance processes to reduce post-production defects by 25% or more
- Develop testing management programmes that reduce test execution time by 15% and testing preparation time by 20%, with 0% defect leakage to production
- Reduce test failures due to unavailable test data by 70%
- Achieve 15% overall project savings through the use of defect prediction modelling