Test Data Issues and the DevOps Process

Test Data Issues and the DevOps Process

Test data is used throughout the software development life cycle. DevOps teams cannot work without test data. Still, people hear DevOps is slowing down and sometimes fails as a result of test data. So, where does the problem lie? The main problem is using stale, polluted, and dirty test data. Bad test data is a hinder in ensuring the quality. Please read this blog to know how bad data affects DevOps and how to get rid of them.

Problems with Bad Test Data

Organizations are continuously adopting DevOps to improve quality and speed. Automation allows running tests in a short time and quick delivery of the software. Autotest ensures no code error. Still, quality is not improving. In part, this is due to using bad data to test and generate the code. People are more focused on automation and speed than using good data to ensure quality. Thus they are not achieving the expected result. Here are some problems of bad data and how to fix them:

Infinity Loop for DevOps

The infinity loop of DevOps is a commonly used word. But it is one of the reasons for failure. In the circle, developers test the data in a single step in the continuum. Shifting left means completing the test earlier, and shifting right means to test in the production. Unfortunately, none of them are effective enough. Developers need to understand and rethink the process of infinity loop in quality and testing. Before using it in each step, members need to be more specific about testing and quality.

Quality Testing is more than Automated Testing

Organizations often misunderstand the word continuous and automated testing. Continuous testing means focusing on continuously improving testing and quality. Auto testing speeds up the process, but poor or not having a strategy decreases the quality. Here are some ways to ensure quality testing:

· Developers need to understand the process, identify risks, and plan to control them.

· Build and deploy code and autotest (infrastructure, CI/CD pipeline, quality attributes, etc.)

· Release the product in the market, observe how customers use it, and learn to improve from the feedback.

Lack of Planning

The main reason for failure in testing data is a lack of planning, training, and skills. Organizations are trying to be more agile. Before doing that, every member needs to understand the practices and set their mindset. To be agile, developers need to focus on software architecture. If developers are not skilled enough, organizations first need to focus on building skills. Otherwise, they will have to waste time debugging the software.

What Types of Data are needed for DevOps?

DevOps requires broad and representative data for unit testing, ensuring security, improving performance, and integration. Here are some facts to consider while collecting test data:

· Domain and types of industry.

· Constraints from the regulatory team.

· Levels of governmental classification to apply.

· Teams’ knowledge about architecture, quality attribution, and customers’ expectation.

Developers, QA testers, security testers, and database administrators must make the test data.

· Developers need to know what the code looks like and focus on writing new features.

· QA testers find data using a spreadsheet.

· Security testers use manual processes and focus on generating security test data.

· Administrators collect production data using masking techniques.

Members can use a mixed build, borrowing, and buying approach to collect the most effective data set. Teams can build data using both manual and automatic processes. While making data, developers should apply business rules, using tools and techniques to generate robust data. Our members can borrow data by selecting and masking. Another effective way is to buy data from other organizations, data brokers, or publicly available data. Avoiding invalid data is also a must.

Delivering quality requires quality test data. Quality test data, tools, and integration is the ultimate reason for DevOps success.