Since we are quickly progressing on the path of digital transformation with businesses and operations digitizing through application and software technology, the need for uninterrupted and consistent output has pushed the testing needs forward.
Sticking to the definition, Testing in Production is a part of the software development lifecycle where any new code is tested on live traffic rather than testing on staging. In other words, Testing in Production is more of a real-time test approach that complements the objectives surrounding continuous delivery.
And therefore, to complement the goals surrounding end-user experience and improving the tech landscape called for early testing or, say, Testing in Production, which is rapidly becoming a part of quality assurance solutions integrated to any development lifecycle.
Under this, the production software, i.e., a version of the original software, is released to live users to collect actual performance data. Unlike routine testing, where software is tested during the development, staging, and pre-production stages, testing in production allows faster releases and rapid deployment.
In this blog, we will aim to unearth the various risks in software delivery and learn how testing in production could work as an aid.
Understanding Risks In Software Delivery
In its simplest form, risks in software delivery are confined to taking software to the market that does not behave as expected regarding the end-user experience. The primary reason for such risks is the introduction of changes to the code or the functionalities.
Going back to the earliest days of software development, the process of risk identification fundamentally contained the idea of adding efforts to eliminate the risks. And even if the risks are not eliminated, risk identification minimizes the chances of failure to the maximum. However, the process involved massive upfront costs that worked as dividends to risk mitigation.
With time, it was realized that extensive planning was done toward risk identification rather than increasing the risks. Especially when software engineering included the concept of SaaS deployments, app store submissions, and other factors, it only made way for more code to move in. Ultimately, it added to lead time and, thus, greater surface area for failures with growing costs, risks, and hampered releases.
The frequent need for change turned out to be the culprit for risk development. However, introducing fewer changes, vetted designs, extensive sign-offs, and massive QA cycles are insufficient to create future-ready technology. But the increase in batch side and code is somewhere calling for more risk and therefore adding to the delayed deliveries and time involved in fixing the issues.
With that being said, let us quickly jump on learning in detail about the rising cost and risk of pre-production testing:
The Changing Dynamics of Software Development
The tech-driven business world involves more and more software technology, irrespective of the niche. Be it healthcare, finance, or the retail industry, the software teams are looking for rapid optimization and faster deliveries. And it has even created space for some other goals along:
Though these factors helped optimize the software development and testing services, they created a gap in the validation process in the pre-production environment. Moreover, these factors have even added cost to the development process and, thus, to the overall pre-production process of testing.
Use Of Third-Party Tools
The simultaneous use of different tools makes it difficult for testers to be sure of the changes in the code. Also, the modern form of development involves using large frameworks, which will likely make way for shared services and libraries to take space along with the codebase.
Though these resources might appear efficient for the development process, they are comparatively less predictable than manual code generated. Moreover, the use of third-party services could even cause slight variations in the behavior affecting real insights into the changes that might hamper the user experience.
The introduction of microservice architectures has allowed moving the complexity out of monolithic software technologies. Minimizing the software has always remained a task for developers that only added to the complexity of the technology. Instead, the technology-enabled interactions between different pieces of software.
All in all, microservices are made to work independently. However, it adds to the dependencies of the developers managing the services when thorough testing is required. It ultimately adds to the costs and makes it more complex to attain independence and agility in pushing changes forward. Also, it is vital to yield complete confidence in the outputs derived from the tests to foster real-world performance. Microservices require setting up the entire environment to ensure no change triggers the risks.
Larger And Larger Data Sets
As an industry, we’ve become obsessed with data-driven applications. Along with our goal of collecting as much data as possible to improve the capabilities of our applications, we’re also subjecting ourselves and our systems to processing these ever-larger data scales. Storage is cheaper, and computers are fast. But with increasingly large data sets comes the responsibility of managing larger test data sets as well: with appreciable costs related to moving them around, storing, and archiving them. This also affects the cost and feasibility of thorough pre-production testing.
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Predicting, Constraining, And Embracing Risk
Over time, engineers, developers, and testers realized how slicing changes in increments could reduce the risk of change. It introduced the concept of frequent yet more minor changes to curb unexpected failures. On top of that, agile methodologies, CI/CD pipeline, and TDD allowed for reducing bug prevention within the production environment, cutting any downtime for end users.
As a part of the implementation process, making more minor changes to code may appear like adding so many pieces of code to the original codebase. However, the process enabled comprehensive test coverage with confidence to move quickly with immediate testing and validation. It ultimately helped lightweight planning and shortened feedback loops, with access to real-time feedback and secure coding.
But, Why Test in Production?
Traditionally, every IT solution provider or developer used to ensure that the software build must be tested for bugs thoroughly during development, staging, & pre-production. The process enabled early bug detection and error removal, which ultimately complements user satisfaction and trust.
Nevertheless, the concept of testing during the development process is not easy. Engineering teams must make dedicated efforts to create unit tests and test suites, select tools, and frameworks, and simulate the entire production environment.
Besides, it even requires manual verification of the user flow with sample data to find bugs or issues that users might encounter during production, even when automation tools are involved. And with all that effort, there are still very fair chances that a user might experience buggy software after hours of effort and hard work testing in development.
Feature Flags & Testing in Production
Testing in production is carried out through feature flags. Feature flags feature toggles and rollouts that allow engineering teams to showcase certain features to the live audience as part of the experiment. The data observed from feature flags allows quick verification of software or application capabilities allowing safe rollback for unidentified issues.
Besides, testing in a production done through feature flags or minor rollouts allows checking any dependencies and edge cases for comprehensive testing. Similarly, real-world data makes testing more powerful complementing load and performance benchmarks.
Moreover, feature flag tooling even allows A/B testing, where every feature can be tested and compared to its older versions to analyze the product for optimum user experience. It enables the creation of bug-free software and allows validation that comes through real data.
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