Provide Test Data

Problem Statement

Getting data to test a newly developed application is complicated and time-consuming. Using production data to test an application may lead to the following problems.

  1. Getting production data
    Getting access to production data requires accessing the production system and pulling data from it while the system is running. Pulling a greater amount of data from a running production system may have negative effects on its normal operation (e.g., I/O bottlenecks, network bottlenecks, latency problems, significant lower throughput).
  2. Using production data for testing
    Testing with production data is often considered to be the “true” testing of an application. But more often then not, production data may not be accurate to test a software’s new feature. For example, the amount of data pulled from the production system is too small (if testing, e.g., big data processing capability of a new software) or too big (far too many different cases within the data when the software is in an early delevelopment stage).
    Another problem might arise if the new software should specifically extract a new feature from the data set. It might not be clear beforehand if the production data contain the pattern the new software searches for.
  3. Handling sensitive information in production data
    Production data may also contain sensitive information. Such information has to be masked before it can be used to test a new software. This process is time-consuming and there is a risk that sensitive data will be accidently exposed to other parties.

bankmark’s Solution

Using its Parallel Data Generation Framework (PDGF), bankmark is able to provide customized data sets for all software testing needs. The data sets are modeled in collaboration with the customers to assure it fits their individual needs. The data sets can have any size and are modeled to contain any peculiarity the customers need to test their application fast and time efficient. The adaptability of PDGF allows for quick data structure adjustments during the development process to provide updated data sets in case the customers’ software requires changes to the provided data set.