When it comes to the idea of digital transformations, it is the extensive dependency that every business process, as well as routine operations, have on software, application, and websites. However, this change has pushed all business organizations to struggle with brand differentiation that lies within providing a seamless user experience.
In 2020, worldwide spending on digital transformation reached 1.3 trillion U.S. dollars, growing 10.4 per cent year-on-year – a compromised yet still strong growth despite the economic recession caused by the coronavirus (COVID-19) pandemic,” reported by Statista.
“Most organizations are switching towards DevOps that includes an all-new test ecosystem that can handle all the highly integrated and complex API-driven technology.”
Besides, it is necessary for IT managers and QA teams to yield speed, quality, and cost in order to create an all-inclusive digital environment.
From giants like Facebook and Netflix to SMEs across the globe, every organization is aiming to change their engineering practices while keeping shifting their focus at QAOps. And the most part of this change includes a shift to high-volume testing powered by automated testing solutions.
This does not mean to imitate the testing practices, for they are being used by global brands, but automation testing has all the potential to meet the high-speed development and release cycle needs. More importantly, the automation frameworks vary for every organization based on their leadership style, technology stack, team size, and organizational structure.
Therefore, it is vital that testing automation should be fostered in an incremental manner in order to have more mature processes. And this needs QA managers to work on creating a project-specific blueprint of development projects that can assure a pleasing user experience.
In this blog, we will try to highlight how test automation solutions have redefined the digital transformation journey while talking about the changes that test automation requires to complement the digital tsunami.
Though test automation has helped organizations expand the scope of modern software testing, there are still many organizations that need to make it a part of routine operations. It even requires the adoption of pro-responsive test strategies that can help reinforce the business objectives through differentiated services.
However, aligning with such change needs challenging the existing testing models that can outperform the changed development courses made to meet the user feedback. Some of the most significant changes that are necessary to push test automation forward on the path to nurture digital transformations include:
In order to take the concept of digital transformation forward, testers need to look further from code and testing details to the business-level test cases that can help leverage the overall experience. It can be done by looking at the real-time operational data and getting insights from the scope documents and project requirements to create applications. But it can only be made possible with the right selection of tools that can help develop and execute tests to help with the most precise metrics surrounding the end-user interactions.
In other words, digital transformation demands testers and developers to aim at test strategies that are customer-centric and can reflect well on the customer’s point of view. Such practice can be considered as Behavior Driven Development as it encourages the QA teams to focus on end results and overcome any gap between engineering, user requirements, and the perspective of the business teams.
The idea of digital transformation is beyond responsiveness as it needs testers to have foresightedness to future requirements and change. The only way to align with such requirements is to focus on continuous testing in order to test every single new feature at the coding and development stage to align with the futuristic engineering goals.
However, the introduction of continuous testing into the existing development and testing environment needs automation and tools that have the capacity to provide continuous testing at the developer level. Since software development companies have to bear the challenge of test data generation and management, automation could help testers to overcome all the manual tasks and time-consuming efforts by fetching data in parallel to the development.
Meanwhile, test automation can also be leveraged in the testing of newly built codes to have timely feedback on all errors and bugs, complimenting the quality assurance continuity within the software development lifecycle.
Manual testing is error-prone and laborious, which means it has greater chances of testers being trapped by forgetfulness and skipping of any tasks that can increase redundancy with the projects. On the other hand, using automation and machine learning-based test solutions to handle any specific operations negates any chances of skipping or forgetting any test metrics or activities that can deplete performance data and results.
Also, automation tests are an essential component of digital age continuous testing. Though it is not feasible to yield 100 per cent automation, the most complex applications can be targeted to attain 80-90 per cent automation by working on tasks like test data generation, test data management, executing test cases, managing test suites, and generating test reports.
In short, test automation is a task that demands responsible actions, and it is important that testers and developers should collaborate on automating the right tests in the first go to have the most well-defined test strategies. QA testers can even try to work on mini regression suites to cover any operations that are business-critical to build the initial confidence and then aim for a complete test suite run for faster and effective results.
All in all, there must be thoroughly defined acceptance and assurance criteria for every use case to ensure the applications are highly functional and are adaptive to any scalability requirements of the future.
Automation testing services needs the right treatment on the development and QA part, as testing is an activity that demands both development and testing skills to identify and fix any code issues. It would demand businesses looking at the digital transformation to invest in the right skill set and tools in order to make every step towards automation completely hassle-free.
To achieve all such proficiency, it is necessary to devote the right budget to testing and highlight the important needs of the software development lifecycle with the objective of supporting all the critical functions of futuristic development.
The futuristic development demands methodologies that can help improve the user interactions while assisting the testers on all the limitations of existing test automation processes. Since testers at present use only the test scripts with assumptions on the most likely interactions pursued by the users, it is vital to understand the in-depth perspective of end-users to cover the entire journey of the user through test automation scripts. And it is actually one of the biggest concerns with current test automation practice.
One way to overcome the existing fallacies is bringing AI into the automation practices by implementing algorithms to data produced during testing activities. This can even aid testers on all the manual and repetitive tasks by shifting on matured CI integrated functional test suites to aid end-to-end testing. However, it only needs QA teams, DevOps, and IT managers to explore all the areas of application where AI & Big Data could complement automation. Also, establishing such practices can even aid the testing of features, usability, and integrations with detailed test data analysis developing a knowledge-base for future-oriented and proactive development.
The present situation demands accelerating the business under the light the digital transformation. However, meeting the test automation goals needs both DevOps and QA teams to reflect on key perspectives of the futuristic development.
Since DevOps could help expand the business benefits related to revenue and lowering the operational costs, approaching DevOps with automation testing could help meet the customer experience with respect to a business point of view.
On top of that, release management activities like static code analysis, testing, and deployment needs automation to work on scripts and tools enhancing the quality of deliverable. Thus, implementing automation whenever and wherever possible could help make a difference to an organization’s capability on continuous advancement and effective releases, making way to achieve the notion of progressive digital transformation.
— Kanika Vatsyayan is Vice-President Delivery and Operations at BugRaptors
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