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Software Testing Automation Tools and Latest Trends 2021

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Introduction to Software Testing Automation

Before moving towards what is inclined in Software Testing Automation, let’s lookup for Automation Testing Myths, which needs to be clear before moving to the upcoming technology enhancements-
  • Is Testing all about Automation?
  • An increase in Automation testing will remove Manual testers.
To answer the above question for Software Testing Automation testing myths, let’s look up what Automation testing is and what we will achieve in upcoming years in automation testing. Testing is of two types.
  1. Manual Testing
  2. Automation testing
To Go through the reality behind the myths mentioned above, Automation testing is responsible for completing repetitive tasks with better accuracy and less time span. So testing is not all about automation. It is the process that creates automation of tasks for which scenarios and cases are prepared manually after understanding the whole working functionality. Manual testers need automation testing to remove the headache of performing repetitive tasks repeatedly with manual efforts, which could be saved to apply in more complex requirements understanding.
Choose a testing tool that can automate applications that you are planning to use — ideally, any application under test (AUT). Source: The 3 Main Test Automation Factors

What are the latest trends in Software Testing Automation?

Below is the structure showing the latest and upcoming years enhancements

Cloud-based Software Testing

Cloud-based testing means testing the software with resources in the cloud. Cloud software testing is necessary after drifting to Cloud, and testing with the cloud is possible with any public, private and hybrid cloud. The area covered in Cloud-based Software automation testing will be-
  • Network-based cloud testing
  • It Tests the coverage of the data flow control over different networks with different bandwidths and tests the successful transfer of data.
  • Infrastructure based cloud Testing
  • It focuses on the failure recovery and storage policies with Secure connection of networks.
  • Application cloud-based testing
  • It covers the functionality, end-to-end working, Browser compatibility, and production workflow with cloud testing.
To look at the benefits of cloud-based testing, we can say that users need to pay for the time the infrastructure is in use, parallel testing over platform, browser, and devices, Enables continuous delivery, and better scalability.

Cloud-based Collaboration Testing Tool

Cloud-based collaboration testing tools allow the geographically located testing professionals to divide their work over the team by creating tasks and prioritizing them to complete the testing effectively and on time. Major usable tools are below with their specialty of making teams work together with cloud technologies-
  • Asana

Asana is a cloud-based Saas to provide a collaborative approach to testing. Many testers can log in and work collaboratively by assigning tasks to each team member and setting their priority. Listing your team board, calendars, and big charts creates your way of work.
  • Jira

This platform Atlassian JIRA is to handle bugs and project issues. With this software's help, roadmaps for project and test management are possible parallelly and at the same place.
  • Trello

Trello is the project management tool beneficial for testing management and provides project information to the trello board. The best quality is to show who is working on what is a clear view.
Software testing is an important process that ensures customer satisfaction in the application. Source: Artificial Intelligence in Software Testing

Artificial Intelligence & Machine Learning

Artificial Intelligence is the computer science technique that introduces intelligent machines with the Machine learning method's help to do the tasks without human efforts with multiple & large datasets to learn about the process/software. In the field of software automation testing, it benefits in many forms, such as.
  • It helps in Unit testing by working with RPA, an AI-based technology, to create automated processes to perform any task.
  • Algorithms of AI/ML helps to create the most used test cases and scenarios by reading the application's observation and preparing the data set to train the model for the application's action in certain situations.
  • According to the researchers, Machines can remember visual patterns in a better way. So the AI/ML machines will also help in developing the GUI testing and visual testing tasks.

Demand for IoT & Big Data Testing

IoT which refers to the Internet of things is the science which connects the internet connectivity to household objects such as Voice controllers, Air quality monitor, wireless high-speed internet, Connected appliances, electric farming equipment, Wearable health equipment, Smart factory equipment, Smart home security systems, Wireless inventory trackers, Biometric cyber-security scanners and a lot more. Testings which is most importantly in IoT’s are-
  • Security and connectivity testing
  • Integration Testing
  • Network testing

Blockchain Testing

Blockchain is a secure and indestructible digital record book with cryptography and encryption of data to link different records or blocks in the chain. Type of testing done with blockchains are
  • Node testing
  • Integration testing
  • Functional testing
  • API Testing
  • Performance testing
  • Security Testing
Things to consider while testing Blockchain are-
  • The maximum Block size should be 1 megabyte, and testers need to keep this in mind while automating blockchain testing.
  •  Chain size
  •  Connection of Blocks
  •  Encryption And Decryption of data

Robotic Process Automation

RPA Testing makes the time-consuming tasks to be completed early and can be performed with any manual attention. To create a task for testing in RPA, it consists of already created processes that only need to be dragged and dropped to prepare a testing process.
  • It makes testing tasks easy by a codeless process which helps in the ease of maintaining the syntax.
  • the accuracy is achieved for all the testing tasks as the work performed by bots, not humans.
  • Productivity is increased as the time consuming is reduced and the number of executions increased.

Demands for Cybersecurity & Risk Compliance testing

As digital competence increases, the number of threats to cyberattacks is also rising. Security is not only securing the transactions but also protecting the end-to end-users privacy. As the digital world is increasing day by day, cyber-attacks will be ongoing at a higher level in the upcoming years. The security testing approaches enhancements will be a constant hot topic for several more years.

Autoscaling CI/CD to support parallel testing

Only one developing branch is used to execute the CI/CD pipeline build's monolithic approach. Suppose any bug or error causes delays in product or application delivery and needs more application changes to resolve the issue. Autoscaling of CI/CD parallels the parallel execution of multiple feature branches with a developed branch to handle this issue. On the count of tests that need to run under the pipeline, the grid-scale hub increases the nodes' count in use and activates the hub's connectivity. Such Autoscaling of the pipelines will make a huge increase in the testing outcomes for a particular application.
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A wider skill set is needed for upcoming changes in the Testing world

On looking at the increase in It industry with evolving technologies such as
  • Element discovering approach
  • API understanding of how the Elements are working
  • Configuration handling
  • Automation tools understanding
  • Knowledge of Functional testing
  • Test plan creation and document creation
With the understanding and knowledge of the above terms, one can cope with the emerging technologies of Software automation testing enhancements.


As the world is changing and enhancements in each sector are happening. The above are the technologies/Processes which are evolving the testing infrastructure such as AI/ML, Autoscaling of CI/CD, Cloud Testing, IoT Testing at a larger scale to work. Testers need to enhance their skills according to the listed skill set to manage testing process changes.

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