Test automation can provide great benefits to the software testing process and if correctly utilized and integrated into the overall test strategy; can provide great benefits and ongoing returns to a client. DTS recommends a mixed test solution for most client requirements, as a certain percentage of a testing solution usually needs manual intervention.
Manual testing alone has several disadvantages:
- Slow and costly
- Not easily scalable
- Not consistent and repeatable
- Difficult to manage
- IP remains with the testers
The DTS automated testing services provide the following benefits:
- Defects are identified early in the test cycles thus providing more time for resolution
- Test execution can run 24/7
- Allows more time to be spent on ensuring quality before deployment and thus less time on maintenance
- Facilitates effective utilization of budget due to on-time delivery
- Reduces the need for manual testers
- Permits effective utilization and better focus of non-automated testing assets
- Allows greater test coverage within budget due to increased speed and reduced costs
- Gives back time to the development team to focus on new, better solutions rather than wasting time on maintenance and repeating defect resolution
- Creates a reusable test basis that can be utilized in regression testing and future testing initiatives.
DTS bases its testing solutions on a unique framework that has been designed to leverage the benefits of automated testing whilst providing results and metrics that are directly mapped back to specific functional and technical requirements.
This mechanism provides clients with a never before seen level of transparency that allows for fast, accurate and in-depth quality measurement of solutions under test.
The Test Automation offering
- Core Test Automation
- Automation Management
Investment and Costing
The DTS Service Offerings are broken down into separate costs based on engagement model, services requires and any specific client requirements. DTS offers either Fixed Cost Or Rate Based costing models or a hybrid of the two.