How to Ensure Effective TDM in Continuous Test Automation
Automation of your data in DevOps doesn't just happen without an effective management process.
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Test Data Management prevents your Continuous Test Automation strategy from going into the dump. Wasn't automation supposed to take over redundant processes, lessen the burden on manual testers, and speed up the entire testing cycle? If testers are still required to keep updating the test data every time for the test to run successfully, doesn't that defy the whole purpose of automation and further weigh down poor testers under a load of tedious tasks?
DevOps is teaming up with automation to encourage continuous processes of integration, testing, and delivery. The dependency of DevOps on automation is immense. While everyone is seeing the glittering on-stage drama of DevOps backed by automated testing, they are mostly ignorant of what goes on backstage. Like a puppeteer who seamlessly orchestrates the movement of the props for presenting a successful puppet show, test data determines the successful completion of a software test. Now, what if the material of the puppet thread used is changed without informing the puppeteer that he needs to wear additional protection to avoid cuts? He would injure himself, which might cause the show to halt, resulting in failure. The agility of continuous testing does not provide the scope for conveying the change in time, making implementation of proper test data management practices a necessity for software testing.
The IT industry is exploding with data. In order to handle such large volumes and to derive real value from the collected data, a well-thought, carefully-devised strategy is imperative. With Test Data Management, it becomes feasible for enterprises to sensibly use that data without getting overwhelmed or lost. Automation has become the bread and butter of modern software development and testing methodologies. Therefore, it is important to meticulously lay out an automation strategy with the help of foolproof management, so that all the pieces fit together to complete the puzzle of software testing.
Handling the Heaps of Data
Test data management requires proactiveness, and so does a productive continuous test automation strategy. By taking care of a few mundane tasks at the beginning, automated tests can run smoothly for all the subsequent courses.
The data collected usually comes from various sources and may be stored in a variety of formats. Streamlining this multi-type, multi-source data is the primary step for building a beneficial database. This requires extensive exploration, sifting, and categorizing to identify what is worthy and what is not. TDM is extremely efficacious in taking care of such exasperating activities. A TDM tool can process through the data behemoth and produce reports that will ultimately bring value to the overall quality of a software.
It is also crucial to safeguard the data from breaches. As cybersecurity and data leaks are becoming the prime concern, TDM can help sealing the gaps which might allow hackers to penetrate and release sensitive information.
TDM for Near-Genuine Test Data
Test Data Management is a process of planning, designing, storing, and managing software or quality testing processes and methodologies. Despite the high quality, it is not feasible to use production data for testing purposes due to regulatory and security concerns. Therefore, it must be masked or made de-identifiable in order to retain privacy while not compromising with the quality. TDM helps create potent test data through:
- Exploring the voluminous data from various forms, formats, and systems to detect common test data patterns and create desired data subsets for a test
- Validating the subsetted data by removing redundancies and masking the confidential information
- Enabling integrated sensitive data discovery to identify data that needs to be protected
- Prioritizing valuable data and allocating it to the respective tests for successful runs
- Implementing business rules and policies to preserve the confidentiality of data
- Regularly updating the old data for making them reusable in future tests
- Automating all these tasks to increase efficiency and speed
Maintenance Is the Key to Effective Test Data Management
By segregating test data from production data, TDM not only reduces the sheer size of the available data but also processes that data to generate beneficial testing reports. This data needs to be refreshed and updated on a regular basis to sustain its relevance with changing codes of the software. Data virtualization and externalization can be of great help when it comes to data maintenance. Storing the data externally prevents it from getting affected when the software or code is updated. It is also easier to make changes in the external data file instead of the lengthy codes.
It is required that old data is cleared, and new information is added frequently to the data repositories. These additions and deletions ensure that the data stay relevant with time and can be utilized for the coming years. Test data management eradicates the need for creating new data again and again.
Best Tools to Manage Test Data Optimally
Test data management is as tiring as it is critical, which is why automation is indispensable. There are several tools available to translate an on-paper TDM strategy into reality. Informatica, DATPROF, Compuware, HP, Delphix, Solix EDMS are just a few examples of the options available in market. While choosing a TDM tool, the C-suite must keep their business objectives in mind and select the tool that aligns with their ideologies. Designed to reduce testing costs, improve test results, and enhance productivity, TDM tools make it certain that continuous test automation does not get obstructed due to weak data.
Continuity and automation are embedded at the core of modern-day DevOps and Agile methodologies. Automation feeds on data. This data must be accurate and of the best quality to avoid any breakage in the continuity. A meticulous Test Data Management strategy transforms into productive DevOps implementation and defect-free completion of testing cycles.
At Cigniti, we have implemented robust test data management practices for complex enterprise platforms. With over 200 experts in the Data Governance practice, Cigniti supports organizations in the implementation of solutions for centralized data governance, automated data management, data analysis, and identification and high availability of test data.
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