When we ask customers what they expect from the test automation, the most common answer is that they want to reduce the time consumed to test the software before a release.
On the “Heads” side of the coin, it is one tip of the iceberg in terms of benefits that you can acquire from test automation. Of course, reaching the goal with manual test cycle is burdensome and prone to errors , It doesn't allow you to reuse and automate the test scripts too. On the ‘Tails’ side of the coin, you start searching for other benefits of test automation. But let’s see how the time reduction goal for a test cycle became so much crucial in recent years, how the value of test automation gets increased, and what emerging trends are needed to introduce in test automation for 2021.
Evolving Test Automation Trends for 2021
Agile Software Development Completely Replaced Waterfall Methodology
Having said that, many companies already used test automation years ago, but it was not much popular as today. There are various reasons for this. Still, without putting much pressure on mind, we can understand the transformation from the conventional waterfall method to Agile software development contributed a lot to become the urgent need for test automation.
In the traditional waterfall-based concept, working on software projects was demonstrated as a one-time thing. It was similar to developing a bridge, where first we had a plan and design. Later, we moved to the build and testing process to fix any minor issues in the project and ensure that the end product would meet the quality standards. During that process, we assumed that if engineering and planning were done appropriately, then it could be easy to resolve some programming issues. After some years, we realized that the waterfall model is not capable of fulfilling its promise.
Due to the growing complexity of software projects, it was not possible to rely permanently on the waterfall model. Moreover, customization is the new market trend that allows customers to make changes in the application whenever they need it. In this case, it is not feasible to close the technical details or plan many more features of the project in advance. Apart from that, it is impossible to predict the next new technologies.
Business needs also get changed from time-to-time. Now, you may clear why having an Agile-based software development or mindset becomes so essential in test automation. It helps us respond quickly to customers and allows us to deliver multiple projects in a single month. One can fix bugs a number of times, improve its team morale, and obtain the benefits like customer satisfaction, continuous improvement, better control, superior quality products, and even reduce the chances of future risks.
Hassle-Free Script less or Codeless Automated Testing
Codeless automation testing is one of the top test automation trends that you can watch out for 2021. Several codeless automation testing tools are ready for QA specialists in the market, such as Perfecto, TestCraft, Katalon Studio, CloudQA, Selenium IDE, and TestCraft that you can use to experience much more productivity in the codeless test automation journey.
Accomplishing test automation at a scale is the cornerstone to experience progress toward a continuous deployment release tempo. In this case, taking the risk of writing multiple lines of code for test automation can be a laborious or time-consuming process for even the most proficient SDETs (software development engineers in test).
When using codeless testing tools, it becomes more convenient to generate easy test cases because it is based on visual modeling and crafted using robust AI technology. Some of the lucrative benefits of codeless testing tools are as follow:
Tests can be developed easily and quickly.
Even creating test reports as per the company’s requirements is simple.
Trouble-free integration experience offerings for test management tools and bug tracking.
Affordable compared to traditional automation testing tools.
No need to create tests manually. Testers can test the software’s functionality faster and perform its evaluation with the utmost ease.
Related Read: Software Testing With AI
Hyper Automation – Providing the Crucial Edge to Customers in Digital Transformation
The ability to enhance customer experience, quality of employee engagement, and the need to improve service and operational performances are some of the more influential drivers of hyper-automation that is sometimes known as cognitive or smart automation.
Many enterprises today focus on providing safety and business continuity to customers. All over the world, people are working remotely. They are struggling a lot to generate handsome revenue through different mediums and planning to boost their transformation process using digital technologies like Big Data Analytics, Data & Analytics, Artificial Intelligence, Deep Learning-Based Image Recognition, Artificial Neural Networks, Natural Language Processing, and Statistical Machine Learning.
These are some emerging technologies added to automation to help organizations implement several tasks with high-efficiency and accuracy. Also, it provides us with better outcomes for businesses and lets our customers get a world-class product experience.
Refactoring Helps Reduce Complexity in Testing
It is not feasible always to develop new features for customers at an inexpensive cost because the price of the software can be increased according to the complexity or latest technological demands of the project. The solution for reducing accidental complexity is refactoring, which is the strategy to improve the internal design/internal structure or a piece of the software without considering its external behavior.
Refactoring is one of the best innovations in test automation because it helps us enhance the software’s design bit by bit without redesigning the entire system. There are numerous refactoring tools available on the web. Still, even after having the popular refactoring tool, some developers introduce new bugs in the system and leave the bug for software hackers.
In order to give steady deliveries, additional features, and quality projects, one needs to do refactoring with detailed regression testing because it helps keep complexity under control and helps teams find and remove defects from the software development life cycle early as possible.
Continuous Improvements in Software Testing
The thing that attracts everyone in the software company is how test automation maintains its relationships better with all facets in the development cycle. Other than productivity and quality, test automation is related to business processes, the culture, the organizational structure, and the architecture of the product.
Many companies realize that for improving the testing process, it is vital to give the quality for the software application and for all its business processes. However, improving and controlling the test process is not much simple as you supposed because clients give their demands on-demand that one needs to fulfill by hook or crook. If your current test plans are sufficient, still, you need to ensure improvements for the future. You also need to have forward or proactive thinking to review the testing process.
To implement the continuous process model or experience continuous improvements in software testing, test managers have a dire need to focus on the PDCA (Plan-do-check-act) model because it helps you learn from results and set targets, creates a matrix, and identify the improvements.
With the four-step management method, you can also evaluate the efficiency of test improvement actions and analyze how much you save from one project while continuously improving the process. In the end, you can review the improvement activities, standardize the improvement point in the management process documents, and it lets you understand where and when you need to apply changes in the next project.
Robotic Process Automation
Any software bot that you utilize to automate everyday or highly repetitive tasks is called RPA (Robotic Process Automation). In RPA, robots are some software programs that can mimic human actions and make an excellent interaction with your computer apps to have a knowledge of rule-based activities.
Based on pre-defined data and rules, robotic process automation shows its strengths while solving complex business problems and helps us make better decisions for our business. RPA is sometimes referred to as intelligent automation as it works based on the latest technologies such as Machine learning, Natural Language Processing, Natural Language Generation, and Computer Vision.
When using RPA as one of the top test automation trends in 2021, it promises to provide the following benefits:
Less Human Errors: While doing data entry like tasks or maintaining similar records into multiple systems, we always choose copy-pasting work because it saves our time and helps us complete our task faster. Still, as a result, it increases the chances of errors and sometimes puts us in a bad situation. However, RPA-based tools are superior to eliminate human-made errors and versatile enough to manage simple to complex testing activities.
Improve Time to Market: Instead of completing your application task in a month, RPA allows you to develop, test, and deploy various activities within hours and ensures to give high value to your customers.
Agility & Speed: Due to the increased use of robot-based resources and less human involvement, you can efficiently perform the same task at high speed. Apart from that, RPA helps you get reports or detailed insights to make more informed decisions in business. Even it gives you a recording feature to plan some new things for the future.
Better Customer Service and Management: From deployment to monitoring, you will get a centralized platform through RPA to give your customers 24/7 service, and they will be able to generate a huge ROI.
No Previous Programming/Coding Language Required: At some RPA platforms, you can have a design in the form of flowcharts to focus on productivity and high-quality work. It helps you handle automated tasks, so you don’t need to worry about learning previous or past-based programming languages.