Managing the project lifecycle effectively requires that a dynamic analysis of risk is implemented, ensuring that risks are continually being minimized whilst cost and time delays caused by the implementation of the testing process are kept to a minimum.
A well-managed testing process provides organizations with visibility of associated risks, and will help quantify and qualify those risks. By identifying and understanding these risks at an early stage, an organization will have sufficient time to manage them and minimize their effect. The testing process, when carried out effectively, will itself provide an effective risk management methodology.
Validata SAS risk-based approach ensures that operational risks are identified and prioritized to ensure the issues are fully addressed. By adjusting the testing effort to accommodate the emerging risks, helps maintain control of systems and mitigate the major business risks.
Validata SAS employs a modeling method to define the structure of its repository and how it is accessed. In effect, the model represents the tool’s behavior and the test assets it stores.
Why is this model-based approach so efficient?
It’s high level, it’s industrial and it’s flexible.
Test modeling is more about high-level test design than low-level test coding. Combining Test Data & Expected Results with Real world testing scenarios, scheduling concurrent user simulations and creating flexible execution strategies, this high level approach saves a lot of redundant and repetitive work.
By industrial we mean testing is automatic, repeatable and testing logs, documentation & reports are produced automatically.
The distinctive feature from other modelling approaches is that the relationships between objects are treated themselves as objects. This allows complete independence of logical and physical data models.
This data-centric modelling technique in Validata SAS is well-suited to mapping source data to a new target. This is perfect for Data migration projects that are characterized by use of large volumes of production data for testing, which often have semantic differences across systems that need to be reconciled.
Validata SAS allows you to create transformation rules that range from simple one-to-one mapping schemas to mapping of complex hierarchies containing associated Business Object Definitions(BODs). This feature allows the tool’s mapping Engine to transform from any complex data source to another complex data source, utilizing internal and external transformation Rules in the process. The associations between BODs and external structures can be modeled: starting from flat files to XML structures.
Consider a scenario of comparing customer data stored in a credit scoring application to data about the same person stored in a core banking application like T24. Using a mapping schema Validata SAS can extract the customer legal ID from his/her credit scoring record and check if a CUSTOMER record with such legal ID exists in T24..
Our unique data-centric approach to test design minimizes effort and repetitive steps, and maximizes test coverage. This approach is also extended in test execution, with techniques such as multi-release testing, model-driven testing alleviating key pain points experienced by testers today.
We maintain a dedicated team to deliver implementation, training and consultancy services. The team comprises of product specialists, used either on short term assignments primarily to assist with the early days of the client engagement, or for delivery of complex solutions.
This team delivers the Quick Start approach which follows a proven plan to maximize results in the minimum time. It has been developed to accelerate the ROI, getting the product operational in the quickest possible time under a structured programme of activities.
Prior to the QuickStart engagement, Validata Group will confirm with the client which skilled resources are best for the project. We can provide the services for both technical and business resources, depending on your requirements.