This post is from the Experitest blog and has not been updated since the original publish date.
The End-to-End Guide to Continuous Testing
DevOps practices are continuously evolving. We are already seeing the benefits of end-to-end test automation to integrate QA into existing Dev Ops practices. It is time that we took steps to expand our horizons. Continuous testing in a DevOps environment takes test automation to a level never achieved before. With continuous testing, we are now able to test earlier, test often, test faster, and automate. The result is unprecedented levels of quality and efficiency.
But continuous testing without data is always incomplete. For continuous testing to be faster, better, and cheaper, it has to be data-driven at its core. In an agile and DevOps world where data is getting bigger and more complex than ever, employing it in planning, designing, and executing testing is a growing challenge. Data analytics facilitate continuous testing in a fast-paced delivery environment. They eliminate much of the hurdle developers and testers face in monitoring and deploying their QA programs.
So, to get them up to speed with agile and DevOps practices, they need a continuous testing approach which is backed by well-rounded data analytics to gain end-to-end quality insights.
What is Continuous Testing?
DevOps and Agile are about transforming people, processes, and technologies to deliver innovative solutions at a rapid pace. Continuous testing is about achieving unmatched quality. In other words, with continuous testing, organizations are in a better position to achieve both "speed" and "quality" when delivering applications at scale.
Continuous testing, as the term suggests, is undisrupted testing carried out on a continuous basis. In a continuous development environment, a release candidate (software change) is not a one-time process. It is part of a continuous flow -- in that, it continuously moves from development to testing to deployment. Unlike the old approach where feedback system is generally sporadic and test data management is loosely defined, continuous testing in the modern-day relies on actionable feedback at each stage of the application delivery cycle. As a result, you can achieve a greater level of efficiency in development and deployment. All without having to make any tradeoffs between quality, speed, and costs.
Why Continuous Testing?
Software and applications are increasingly becoming the primary interface for businesses. The risk of failure can translate into sizeable losses. Continuous testing takes into account such risks and provides insights into whether a release candidate has an acceptable level of business risk before being deployed. In addition, traditional tools and methods are no longer sufficient to achieve test automation. Consumer requirements are constantly evolving and industry dynamics are undergoing changes, demanding more from testing.
Today, application architectures require a greater breadth of technology and more reliable support for scalability. Embracing cloud, microservices, and APIs means creating a complex system of different protocols and technologies around a single business transaction. Furthermore, release cycles for applications have shrunk to a matter of days due to Agile, DevOps, and continuous delivery practices. This creates a need for a testing approach which is nimble, less complex, and more supportive of frequent changes to codes.
The whole point of continuous testing is to achieve the desired level of "speed" and "quality" in application releases without having to pay too much in "cost". While Agile and DevOps practices have undeniably become imperative, they do not mitigate the risks associated with application failures. And as it happens to be true, such failures can have severe business repercussions if they impact the user experience.
Given the high risk of releasing buggy applications into production, the importance of continuous testing cannot be overstated. However, there are challenges that organizations confront when dealing with continuous testing. These challenges are primarily the reason why, according to Forrester, even organizations with agile and DevOps practices are not able to implement continuous testing adequately. Let's see what these challenges are and how they are impacting the application delivery process.
Time and Resources
Time and resources are scarce. Agile and DevOps practices reduce release cycles, at the same time requiring fast feedback as an essential quality control factor. With increased efficiency in production and shorter release cycles comes the problem of limited time for testing. You need to make sure that all the important aspects of your application are well tested. Without causing any delay in the time it takes to market it. That means you need to create a test for every new or altered requirement. Establish a test framework that supports data-driven testing and reuses the existing test. Keep the test framework aligned with the evolving nature of applications, automate advanced use cases and keep them running, and review and interpret large volume of test results. As a result, doing it all in one go takes times and consumes resources if you don't have the right tool in place.
Automating a test in a continuous testing environment is not always the same. Depending on the nature of a release candidate, it varies in complexity and scope. The more complex and technologically critical the automation requirements, the more sophisticated the set-up and orchestration.
In any case, you need to ensure that your testing resources are capable to automate tests across a wide range of technologies and data points. That you have the right data to set up a realistic test which you can drive through a series of steps. You also need to have access to all the systems and technologies required for your tests. Besides, you also need to factor in the human input to flush out any critical defects that may adversely affect the user experience. While test automation is great where repetitive processes are involved, it can barely uncover issues like those related to usability and user experience. These are things that may affect your bottom line and, therefore, cannot be overlooked in the testing process.
Automated testing in a DevOps environment where test codes are created, run, and deployed on a continuous basis is a natural choice. However, automation doesn't completely render manual testing irrelevant. On the contrary, there are still many critical steps where manual testing appears to be the only answer. Another issue with automated testing is the test result itself. It doesn't always provide the risk-based insight required to make a prompt "Yes" or "No" decision.
Therefore, it only makes sense that you should have specific information about the tests that failed or did not execute for you to make a sound release decision. Automated test results that show ‘this many tests failed' and ‘this many didn't execute' don't help much. To ensure that the test ‘failures' or test ‘unexecuted' are not the most critical functionality, you need to involve manual tester in the process. But keeping up with the needed daily testing in a continuous delivery environment is anything but easy. Manual review and assessment of potential business-critical functionalities take time and therefore slows down the delivery cycle.
Most importantly, with agile and DevOps practices, continuous testing is a necessity. And to gain end-to-end quality insights and drive improvements, you need a tool that consolidates all your digital test results in one place. SeeTest's Test Analytics helps you achieve just that! It has a quality dashboard that provides an aerial view of the key information related to your web and mobile applications. This includes test cover rate and test execution status. Viewing the quality of your application and tracking the root causes of the issues that failed is never easier with SeeTest's Test Analytics. With automated root-cause analysis, you can group test failures, correlate failures, weed out repeat failures, identify sensitive areas in your app, and highlight the key factors causing your tests to fail.
All of these help you cut down on manual work and shorten investigation times in performing root cause analysis. In addition, the tool is framework independent and allows you to monitor and analyze your entire QA program from a single spot. It makes decision making easier and faster by streamlining the investigation process. More importantly, it makes collaboration truly convenient in a DevOps and Continuous Delivery environment by:
- Streamlining the fault investigation and making faster resolution possible,
- Centrally managing fault status,
- Consolidating management summary reports,
- Managing investigation workflow.
In short, the Test Analytics is there to help enforce agile principles. This is done by removing the testing bottleneck and enabling organizations to test early, often, automatically, and continuously.To learn more be sure to watch our Selenium Secrets webinar.