How do you ensure test data validity and coverage for different types of testing scenarios?

Generate unique synthetic test data as much as you can. This is a great way for your test environments to behave as close to production systems. Write your own custom java utilities as per business logic or use JS utilities such as facker.js, whichever you pick, data should be fresh and realistic.

Another consideration is invalid data - every production database has a small percentage of bad records. I'm a strong believer in replication production data as much as possible to include the good as well as the bad. Working in performance testing, I would always include some incorrect test records to match production examples, both in number and in nature. If we push continuous positive data through a system, there might be instances where a single bad record can have a detrimental impact on overall performance. Sometimes even the bad can do some good...

In addition to the definition and characteristics described above you should also think in terms of test data qualities that will make life easier. The test data should be ? Realistic – Accurate to real-life scenarios ? Valid – Representative of real-life logic and arrangements ? Versatile – Supportive of the maximum number of scenarios with the minimum amount of data These qualities apply to both positive and negative test cases. It also hints at the importance of maintaining context in your test data; things have to logically relate. Break the context and you may limit or prevent the interpretation of the test's outcome as success or failure.

In my opinion, to ensure the validity and coverage of test data for different scenarios, a detailed test plan should first be defined, identifying all possible scenarios, considering both the most common and edge cases, next, test design techniques such as equivalence partitioning and boundary value analysis are applied; additionally, tests are automated to ensure repeatability and consistency. Finally, continuous review and feedback are conducted to adjust and improve test coverage.

The business is always going to want to test with full production volumes. However, it’s often not possible due to privacy regulatory/compliance concerns or system limitations (throughput/capacity). To meet the gap we can use synthetic data generation (least preferred for anything other than unit testing) or sampling techniques. The value of data coverage is limited only by the sophistication of the sampling supported. The use of data science techniques around data clustering and member management really allows you to smartly identify the minimum data required to meet a tests objectives. As always, SME’s are key in identifying where to slice and how to slice. It’s an interactive process that builds relationships and trust (added bonus).

You need to have a good understand of what Privacy concerns need to be addressed in your TDM. Leverage SME’s and legal to address exposure at multiple levels: ? Governmental Regulatory @ Geo Region (North America), Country (US), Local (CA) ? Industry Regulatory/Compliance – Example: Payment Card Industry (PIC) ? Internal Compliance (Company level) – Data security model held intact – non-grouping/grouping scenarios (User 1 – can only see his client’s data not others) Etc. It gets complicated quickly and is solved through Roles and Responsibilities along with applying techniques like data masking or de-identification. Those techniques come with their own guidelines to consider.

If you want to optimize something, you have already travelled a great deal with success. Don't fix something until it's broken - goes the adage. Initial, Repeatable, Defined, Managed, Optimized - says CMM. So, ensuring better test data which gives us better results, i.e., test coverage, early bug detection, faster test execution, % speed contribution to build process etc. speaks volumes about the maturity of the testing process. You need test data optimization metrics – bugs per lines of code, lifetime of known bugs, size of buggy code, complete volumes of redundant code (throws the same bugs), improvement on refactoring, availability, transparency, identifying fixable bugs with small code size impact, etc.