Continue # Infosys Whitepaper 
 hat,  thus understanding data  flow across systems and  provisioning the right data •  Synthetic data can be created  from the ground up for new  applications •  TDM tools such as Datamaker  can speed up scenario  identification and creation of  the corresponding data sets •  Errors and data corruption  can be reduced by including  defined TDM processes in  the Testing Life Cycle and by  adopting TDM tools •  Clear data security policies  increase data safety and  recoverability •  Well-defined process and  controls for data storage,  archival and retrieval support  future testing requirements External Document © 2018 Infosys Limited 

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 Coverage:  Exposure to all the possible  scenarios or test cases needs to be the  key driving factor for data provisioning in  functional testing. The data provisioned for  this testing must cover: •  Positive scenarios (valid values that  should make the test case pass)  •  Negative scenarios (invalid values that  should result in appropriate error handling) •  Boundary conditions (data values at the  extremities of the possible values) •  All functional flows defined in the  requirement (data for each flow) Low volumes: Single data sets for each of  the scenarios are sufficient for the need.  Repetitive test data for similar test cases  may not be required and can prove to be  a waste of time. This can help reduce the  execution time significantly.<|endoftext|>High reuse: Some test data such as accounts,  client IDs, country codes, etc., can be reused  across test cases to keep the test data pool  optimized. Static and basic transaction data  for an application can be base-lined so as  to be restored or retrieved for maintenance  release testing at regular intervals, depending  on the release frequency.<|endoftext|>Tools or utilities: As utilities have the  capacity to create large volumes of the  same type of data, they may not be very  useful in functional data preparation. If  the tool is capable of creating a spectrum  of data to meet all the data requirements  and the data can be reused across releases,  then it is beneficial to use the utility or tool  for TDM implementation.<|endoftext|>Optimal Environment Size Data in the testing environment should be a  smart subset (a partial extract of the source  data based on filtering criteria) of production  or synthetic data. It should comprise data for  all testing needs and be of minimal size to  avoid hardware and software cost overhead.  Sub-setting referentially intact data in the  right volume into testing environments is  the solution to overcome the problem of  oversized, inefficient and expensive testing  environments. This is the key to reduce the  test execution window and minimize data- related defects with the same root cause.<|endoftext|>Data Requirements Gathering  Process  During test case scripting, the test data  requirements at the test-case level should  be documented and marked as reusable  or non-reusable. This ensures that the test  data required for testing is clear and well  documented. A simple summation of the same  type of test data provides the requirement of  test data that needs to be provisioned.<|endoftext|>A sample test data requirement gathering,  as per this method, has been demonstrated  in Appendix I.  Feasibility Check TDM is suitable for functional testing  projects that: •  Spend over 15% of the testing effort on  data preparation or data rework •  Use regression test cases which are run  repeatedly across releases (so that the test  data methodology identified can be reused)  •  Indicate that a high data coverage TDM  solution can be identified and implemented TDM in Functional Testing Most challenges mentioned earlier are part of the day-to-day struggle of functional  testing. The most commonly seen challenges are: low coverage, high dependency, access  limitations, oversized testing environment, and extended test data sourcing timelines.  Successful implementation of TDM in functional testing projects can alleviate most of  these issues and assure the completeness of testing from the business perspective.<|endoftext|>In functional testing, TDM is governed by numerous factors highlighted below.<|endoftext|>External Document © 2018 Infosys Limited 

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 Test data preparation in performance  testing is most impacted by issues  like large volume requirements  with significant coverage, high data  preparation time, limited environment  availability and short execution windows.  The TDM team with their tools and  techniques can provide solutions for  bulk data generation with quick refresh  cycles ensuring on-time high quality  data provisioning to address these steep  demands.<|endoftext|>Test data for performance testing is  characterized by:.<|endoftext|>High volumes of data: Performance  testing always needs large volumes of test  data by virtue of the way it works. Multiple  users are simultaneously loaded onto an  application to run a flow of test executions.  Hence multiple data sets are required in  parallel, in large numbers, based on the  workload model.  Quick consumption of test data: Since  the load or stress on the application  is induced by multiple users, the data  provisioned for them is consumed rapidly.  This leads to quick exhaustion of large  volumes of the test data.<|endoftext|>Very short data provisioning cycle:  Since the data is consumed very quickly  in performance testing, a new cycle  of execution requires the data to be  replenished before start. This implies that  the TDM strategy must make sure that  the test data can be recreated, extracted  again or provisioned in a short period  of time so as to avoid the impact of the  unavailability of data on the testing cycle.<|endoftext|>Workload distribution: Performance  testing in today’s time does not use one  type of data repeatedly. If the use cases  comprise multiple types of data, with  a corresponding percentage of each  occurrence, a similar workload model is  built for the testing environment. Thus  smart data needs to be provided for such a  workload model which covers the multiple  types. This multiplies the complexity of  data creation.<|endoftext|>Feasibility Check TDM is definitely suitable for most of  the performance testing projects. The  strategy needs to be well designed to  make sure that the challenges listed  above are addressed appropriately and a  satisfactory return on investment (ROI) is  achieved soon.<|endoftext|>TDM in Performance Testing  External Document © 2018 Infosys Limited 

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 Some of the biggest challenges in  automation testing are associated  with creating test data. Included in this  challenge is the inability to create data  quickly from the front-end, fast burning up  of large volumes of data during test runs,  limited access to dynamic data and partial  availability of the environment.  Implementing a well-designed TDM strategy  can support multiple iterations of dynamic  data in short intervals of time by synthetically  creating or extracting data, using TDM tools.<|endoftext|>Test data for automation testing is driven  by factors such as: Automation of test data creation: The  data required for automation testing  is usually created by using one of the  automated processes, either from user  interface (UI) front-end or via create or edit  data operations in the database. These  methods are time-consuming and may  require the automation team to acquire  application as well as domain knowledge.<|endoftext|>Fast consumption of test data: Similar to  performance testing, automation testing  also consumes data at a very quick pace.  Hence the data provisioning strategy must  accommodate fast data creation with a  relatively short life cycle.<|endoftext|>High coverage: As with functional testing  automation testing also requires test data  for each of the automated scenarios. The  data requirement may be restricted to the  regression test pack and yet covers a large  spectrum of data.<|endoftext|>Feasibility Check TDM as process implementation is certainly  recommended for an automation project.  The automation team must provide the  data requirement in clear terms and  the TDM team can ensure provisioning  of this data before the test run begins.  Automation tools have abilities to create,  mock and edit data but the TDM team’s  expertise and tools can add significant  value to the proposition.<|endoftext|>TDM in Automation Testing External Document © 2018 Infosys Limited 

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 Conclusion In functional testing, increasing data  coverage plays a significant role in  providing the TDM value-add. The sheer  volume of test data that is repeatedly  used in the regression suites make it  an important focal area from the ROI  viewpoint. The right TDM tools can help  provision a spectrum of data and ensure  continuous ROI in each cycle.<|endoftext|>TDM implementation in performance  testing projects can deliver quick benefits  and the improvement can be significantly  highlighted as large volumes of similar  data can be created swiftly and efficiently.  Automation testing can benefit from TDM  implementation. Tools such as Quick Test  Professional (QTP) can create data via user  interface but need significant functional  knowledge and are slow in nature. TDM  solutions can save time and cost by  keeping the data ready.  Robust data  creation methods and tools can be used to