Data Automation: An Introduction
Test Data Automation offers a straightforward way to automate test data management, quickly providing on-demand access to the data necessary for rigorous testing: “if’s” or “indices” provide the results that you need with minimal effort. Irrefutable results are what you want from your data-management department. Test Data Automation (TDA) is a cost-effective solution for data-manpower planning, reducing the time-intensive process of testing for breakthrough results and eliminating common data errors.
Tasks can be defined in Excel using any of a variety of perspectives. Each view represents a unique testing strategy and is easily customizable to each project. “if’s” or “indices” return results using a simple set of predetermined criteria specified in the test data management process. The test data management team can use the same data stored in the data automation system to run parallel forex trading tests against real accounts. The results from individual perspectives are combined to form the collective results for each individual test.
Data Quality Assurance (DQA) is the cornerstone of any data automation strategy. Effective DQA methodology relies on defining the expectations of the data integrity department, applying methods to test for those expectations, and monitoring progress to ensure accuracy and maintain commitment to quality throughout the data automation process. Tasks performed by data integrity analysts can include manually verifying and maintaining the integrity of historical data, statistical analysis, and generating quality assurance reports. These analyses can reduce the costs associated with data quality audits and can increase the likelihood that an automated trading strategy will succeed.
Test Data Automation ensures data accuracy by gathering, validating, analyzing and reporting test data as quickly as possible. When data is collected and processed through a standardized data collection and processing infrastructure, data accuracy can be more effectively tracked. A test data automation strategy will include detailed descriptions of the test processes and data models and should provide data validation and verification checks. Validating and verifying test data is essential for achieving test coverage goals and keeping test data quality at its highest level. Data quality teams can validate test data by conducting quality assurance tests, inspecting test results, and reviewing test scripts.
Unit Testing and Integration Testing are important components of an effective data automation solution. Unit testing allows a software program to be tested thoroughly in real-user situations. This level of testing is not feasible with piece-of-function applications and requires the guidance of test engineers. Unit testing helps ensure that a software application as a whole functions correctly under various circumstances. Integration testing is similar to unit testing, but it involves the collaboration of both developers and testers in a joint development environment. Integration tests verify software functionality by comparing it with previously defined and predicted conditions.
Data regression testing is another important component of data automation. Regression testing is an incremental test that helps identify and correct problems in prior software releases. It also helps ensure that future releases maintain the same level of quality. Continuous evaluation and improvement can only be achieved through continuous and careful observation of the production environment. The benefits derived from automated software testing are directly related to the extent of testing that was done, the number of tests that were performed, and the results of those tests.
