30 MAY, 2016 – MAARTEN URBACH
Agile is a very common term, but what are the consequenses of agile for the availability of test data? Or; how can good test data management support of speed up working agile? We discussed this with Anko Tijman.
For an Agile team, it is important that many preconditions are properly met in order to be able to deliver good software. For example, having the right tools, environments and space available. One of these preconditions that must be properly completed is test data. And as a team you want to exert as much influence as possible on test data. Therefore, this is often the reason that test data is created manually. Thereafter it is offered to the application via the front. The disadvantage is that enriching or changing the previously made test data is again more difficult and requires other tools. Getting dba tools and the rights to adjust data is often an organizational challenge. The preparation of test data is often very labor intensive for these reasons.
Test data growth model
In short, test data remains a concern. And that concern will only give more cause for concern in the future. Because the results achieved with Agile and Scrum mean that more and more “projects” are being implemented in an Agile manner.
The result is that more complex Agile systems are being developed. By increasing the complexity of systems that are developed by Agile, the importance of good test data is becoming more and more important. After all, for a simple stand-alone system it is still quite simple to generate test data. But if we have to generate test data across a chain of systems, then that becomes a bigger challenge. In short, the usefulness and necessity of good test data grows as the complexity of the systems increases.
Why is test data so important?
Generating test data across multiple systems is complex, but could still be possible. However, as a system develops further, the demands on the test data become greater. Higher demands mean spending more time on “coming up with” and generating test data. Logical, but undesirable consequence is that you spend more and more time sprinting on preparing test data instead of being able to validate whether the software works. This may have the result that sprints cannot be fully completed.
DATPROF Subset filters the correct test data from the various systems across chains. This makes test automation better able to automate with a limited but self-determined data set.
It is important that good test data leads to better quality of the software, whereby as little time as possible should be spent on creating the test data itself. Ultimately, test data must be facilitative to the development process and you don’t want to have to worry about that as a development team.
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