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Continuous data for continuous testing

Data | Analytics | Big data | Testing

Where are you in your journey towards continuous delivery? Most organizations are in some stage of maturity in moving their software development methods from older waterfall approaches to newer agile methods. Usually, this change is being made to achieve the many promised benefits of a continuous delivery development life cycle: improved project success, better software quality and faster time to market. Let’s face it, better technology capabilities, delivered more rapidly and with improved quality, can give any company a competitive advantage.

The benefits of agile development

Depending on where you are on this journey, you may have come to realize that the DevOps adoption path involves two primary practices: collaborative development and continuous testing. Moreover, continuous testing is the cornerstone to achieving collaborative development and continuous delivery. Testing earlier and more often — continuously — throughout the software development life cycle (SDLC) enables faster feedback on the impact of changes and is a key to delivering higher quality software more rapidly. A central requirement to testing continuously is the availability of the right test data, delivered at the right time, which exactly matches the needs of test cases. And, the “right data” usually doesn’t mean “all the data” that could possibly be available for testing, like full-copy clones of production databases. Too much data can actually impede testing productivity, bogging down agile processes and eating up vital technology resources (like storage).

Experienced testers know that creating, maintaining and refreshing the right test data can easily consume 50 percent of all the time necessary to adequately test new software functionality. It can be difficult and labor intensive to find, acquire and/or create the right combination of test data that exactly meets the ideal cross-section of data values required for fast and effective defect detection during testing.

It, therefore, benefits organizations to keep test data environments lean and agile, containing only the data necessary for effective testing. Testers often copy mass amounts of data from production data sources to test environments in hopes of covering all the possible test case permutations. However, the reality is that these bulky test data environments probably simply elongate agile testing processes while likely having a negative impact on testing quality. While there is a huge amount of (probably redundant) data available for testing, there is no way to know whether it covers all of the necessary conditions necessary to achieve highly effective testing that will result in low-defect software being deployed to production.

Test data management made simpler

IBM recently introduced a new addition to its InfoSphere Optim software portfolio, called IBM® InfoSphere® OptimTM Test Data Orchestrator (TDO),which is focused squarely on addressing the need to more rapidly provision leaner, more impactful test data environments. Optim TDO offers a breakthrough in test data management proficiency by helping customers test only what matters with the smallest amount of test data possible during their agile sprints.

It provides automated features, which enable agile developers and testers to collaborate visually with business users to rapidly build and refresh test data environments that exactly meet continuous testing requirements.

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