As you undertake migration testing, be sure to check all the necessary aspects, which include: Black box testing is less time consuming than white box and does not require knowledge of the implementation or programming. In this instance, the internal structure, design, and implementation of what’s being tested are not known to the testers. It is the behavior testing of the software. Although it’s time consuming, it’s necessary, and it requires knowledge of the implementation and the programming for the migration.īlack box testing is a functional test usually performed by software testers. White box testing is a structural test of the software in which the software developers already know the internal structure, design, and implementation of what’s being tested.

Migration testing is a combination of white box and black box testing procedures. Initiate data cleaning before migrating and then continue to check record counts and seek out errors as you migrate. Moving dirty data from one system to another won’t improve your analytics or help you build smarter business strategies. Without understanding the potential error types, interrogating the system to find those errors, and fixing the data consistently across the system, your data migration project will essentially be rendered useless. Every system inevitably gets junked up with some outdated, invalid, or incomplete data over time. MOST IMPORTANTLY! Clean the data: Plan a data cleaning strategy and execute it! No amount of testing is a substitute for having clean, verified data.Keeping a count: Keep notes on the data and its records before you start migrating so you can check against that information after the migration and ensure no data was lost in the transfer.Keep the results in a log so you can go back after migrating and verify the data that transferred is still accurate. Preparing test cases: Prepare your test cases and scenarios and then execute them with a defined set of users.Studying the schemas and interfaces: How does the new application name fields? Which fields are mandatory? What kind of field level validations are in place? How will the data render in the interface and what do you need to set up to secure it?.Mapping from source to destination: Once scope is established, you must perform higher level data mapping between the source (aka legacy) application and your new application, considering the types of data migrating and the format of that data.How much data will be included? How much will be excluded? Which data will need transformations/conversions to fit the parameters of the new system? Setting the scope: Make sure the team understands the scope of the data you’re trying to migrate.Plus, it’s also quite possible that some of the data migration steps can be eliminated as a result of a better data structure in the new system. There can be a tendency to just bring forward the old data formats because that’s how the data always worked, but it’s a short-term band-aid that can stifle your progress in the new system. Identify current bottlenecks/data management issues: It is quite common to have issues with the way the current data is stored, which might be a result of legacy technology that couldn’t handle certain scenarios.Depending on the industry (medical/financial etc.), an evaluation of compliance rules is necessary to ensure no legal issues are caused by the migration process. Compliance evaluation: A migration can significantly change the way the data is stored.Migrating complex applications requires some prep work, which includes the following:

So, what other testing approaches are there for data migration projects? What should be tested before, during, and after the migration to ensure data is meeting the requirements? Pre-Migration Testing Checklistįor simple migrations, pre-migration testing may seem unnecessary, but most businesses customize and adapt their software solutions to their needs, so the migration process is rarely as simple as plugging one system’s core objects into another. This familiar approach is effective, but it assumes the repeatability of the testing and an acceptable level of error. Sampling pulls a random subset of the data to test, debug, and retest until it meets the necessary requirements.

Most of the time, data testing relies on sampling.

Testing a migration before, during, and after you’ve moved the data is important to safeguard your project’s success.

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