Is Bad Test Data Killing DevOps?

The world of technology can get complicated at times. Especially when you need to match with the feature and functionality-rich software landing into the market with the quickest releases, pairing testing into the DevOps cycle can make things overwhelming.   

However, the reality is the most refined and highly functional software/applications are those made with the best balance of DevOps & QAOps. The urge to launch any product into the highly competitive market at times pushes developers and testers to stick with stale or incomplete test data that comes from the past releases. Also, the lack of adequate data affects the entire idea of fostering testing and agile methodology in the DevOps environment.   

Though it may appear convenient to stick with the traditional development process and keeping the entire focus on DevOps, skipping on the agile testing services diminishes the quality aspect associated with the software. Even if you have testing enrolled into the process with all your focus on code coverage, functionality, and performance, overcoming quality defects can get tricky when the same data is not shared with all your teams. Also, executing tests using old data reduces the scope of taking the most advanced technology to the audience as a team is confined to a limited set of information.   

In short, bad test data kills DevOps. On the other hand, keeping the entire focus on DevOps reduces the scope for quality. And as long as it is concerned with most high-performing applications or software, it is crucial that a balance between DevOps and DevOps testing services must be maintained.  

Let us quickly jump on understanding the most common mistakes related to Test Data and DevOps that might affect the goals related to productivity, end-user experience, and business profitability.   

Use Of Infinity Loop  

The companies that practice DevOps are very familiar with the use of infinity loop, which is the primary reason behind the loss of quality. It not only restricts the QA factor but enables the shift right process that does not support testing during production.   

For instance, infinity loop follows a standard build and release process, which starts with planning, coding, building, testing, releasing, deploying, operating, and monitoring. However, this cuts all the chances of adding specifications at every stage for a smoother transition. All in all, achieving success with the launch of applications needs amplified testing at every stage of DevOps in order to elevate the production and improve the overall releases.  

Practice Holistic Testing  

As long as it is concerned with the traditional meaning of continuous testing, it was more of a combined approach that called for quality factors as well as testing best practices. However, the modern-day implementation of continuous testing has been hijacked by insufficient test practices. The only way to overcome such rapid loss of quality is moving back to the holistic DevOps testing solutions that need testers to focus at:  

• Discover & Plan: This step needs testers to understand all the risks associated with an application or software while working on test assumptions and proceeding towards prototype building.  

• Effective Deployment: The next stage needs testers to instrument code and automates testing for every build working across features, infrastructure, and any other attribute that affects the quality  

• Launch & Evaluate: To launch and observe how users interact with a product.  

Lack of Defect Reproduction Testing  

Most of the time, the failure of any application or software under test is associated with a lack of test plans, skill gaps, and fragmented test cases. However, it is vital for DevOps as well as QAOps to understand that sticking only to agile practices cannot help strengthening the architecture, which is mostly believed to just emerge.   

But the reality is agile practices need testers and developers to not forget the need for Agile testing services into the development and test process. The ability to focus on software architecture could empower the entire software development lifecycle giving command over the test environment.  

Especially when Undo Corporation and Cambridge Judge Business School in 2020 reported the software market to bear a loss of 61 billion USD every year while causing a loss of 620 million developer hours. The amount of loss is expected to surge since testers are not able to reproduce defects leading to unexpected architectural defects.   

Taking Over The Challenge: Balancing QA & DevOps  

When it comes to testing, teams need to have a highly holistic approach to the delivery of applications. This needs testers and developers to understand what a test must accomplish while finding reasons to test every specific feature or capability.   

This would help testers to learn the right process for testing the application while measuring the precision of the test data required to foster quality. Besides, testers, engineers, and business technicians should have an intentional focus on test data for defining its purpose, the process to acquire, and putting the data to use.   

Data for DevOps  

When it comes to software testing, the requirement for data is huge depending on the type of testing, which can be usability, security, integration, performance, or unit testing. Therefore, it is necessary that all the considerations associated with testing must be worked on and are not overlooked. Some of these specifications include:  

• To understand the domain (Healthcare, Insurance, Aviation, etc.) of the product to analyze any special requirements  

• To understand all the regulatory and compliance constraints related to testing data, including personally identifiable information.  

• To ensure that any legal or compliance guidelines surrounding the application/software must be satisfied.  

• To understand all the architecture, user requirements, and quality attributes.   

Involving Team To Create Test Data  

Most of the time, testing holds more importance than the test data, and therefore it needs the complete involvement of the entire testing team. These include testers, developers, administrators, and technical executives. Each of these participants has its own approach to testing data which allows creating a highly secure DevOps Testing solution.   

• Developers could work on white-box testing, ensuring the test data can represent the best cases when it comes to testing new features.   

• Testers can work on the entire production data to ensure all the inherited information and product requirements are justified.   

• Security testers can lean on an ad hoc approach, pivoting the entire idea of security with the application.  

• Lastly, DBAs can exclusively leverage masking techniques bringing production data into lower test environments.   

Production Data: Pitfall & Recovery  

Most of the time, production data is used by both DevOps and Test teams to work on the viability of the product in real-life scenarios. Though the production data is highly accurate, it usually covers around 70-75 percent of the scenarios while missing any user-exclusive test cases.   

On the other hand, the reported use of production data by companies for testing and masking stands at 65 percent and 35 percent, respectively. The approach causes immediate threats to security, increasing the chances of cyber-risks while making it difficult to mask and de-identify. Thus, it is vital that the required test data comes through a blended approach where all the necessary information is generated, borrowed, or purchased, if necessary.  

One way to overcome such hassle with data is using manual techniques, building interactions with front-end teams, and working on synthetic data generation to form the test data suite. This might even include borrowing production data and using it for masking techniques and referencing to prior systems. Moreover, publicly available data can be purchased to scrape any inefficiencies and create robust test data suites.  

Some of the additional approaches that testers might try to improve the acquisition include exploring the data, de-identify, validate, build for reusability, and automate test data generation. Besides, testers and developers can keep their entire focus on blocks to prevent and test data to preserve that allows easy loading/reloading. It is extremely necessary as a test that is not repeatable for its initial state is something that can never help with validations. Besides, your DevOps strategy can be injected with automation to help aid the best of testing and development.  

The Crux  

To align DevOps and QA principles can be tricky at times. However, most of the time, keeping a focus only on DevOps or QAOps principles makes it difficult to address the test data.   

Thus, it is necessary that value must be nurtured through quality. And to deliver quality demands holistic testing that can help with robust test data management, the right selection of test management tools, effective integrations, and streamlining the entire DevSecOps pipeline.  

If you are struggling with agile testing services and need help simplifying the idea of quality, security, and performance for your application, all you need to do is lend a helping hand to our experts at BugRaptors.   

Contact us today.