Test Data Management - What's Innovative?

Across the IT industry businesses are grasping the Gen AI nettle and engineering it into their products. Testing seems to be an excellent area, perhaps even the best area for its application.

The ISO 20022 standard enables reliable collaboration and communication as well as being required by banking standards such as SWIFT.

Platform.sh is a git-based Platform-as-a-Service (PaaS), with YAML configuration and local development with Docker-based DDEV.

Compuware is aiming to make mainframe development accessible to those of us more used to Docker and Selenium

Analytics and graphs are appearing in more and more software tools

Despite appearances mainframes can be agile and can participate in DevOps and continuous delivery.

To keep up with the competition, companies need to get new applications and upgrades to market faster. Testing cannot be a roadblock on this path.

You need to understand how changes might impact on other parts of the system, because the former can break the latter.

In order to automate change you need the data, expected results and test scripts to automatically update, by virtue of their traceability back to requirements.

By building up a library of reusable test assets functionality can be tested more rapidly by selecting components from this library

So called "test automation frameworks" have to be able to respond to constant user demands. This process should be automated.

Load DynamiX's appliance stress-tests large throughputs and infrastructures addressing the "what-ifs"

A look at the solution that Pentaho is putting in place to manage the data asset in a way that befits both the business and technology

Amongst other things (migrations, legacy modernisation, archival) Delphix provides test data management without needing to subset the data

Gird-Tools has announced the release of Agile Designer

GenRocket is a new vendor/product in the test data management space. After Grid-Tools it is the second (so far) vendor to offer synthetic test data generation.

Traditional approaches to test data involve sampling (and then masking) from your existing data. However, there are advantages to generating the data from scratch.