The Top Automation Testing Trends That Every Enterprise Must Leverage In 2021
Software development processes are evolving at a fast pace nowadays, especially with the growing demand for more innovative applications and software. This has led to the introduction and adoption of agile methodologies, followed by the increasing demand and acceptance of DevOps. All this can together work effectively only if the machines are capable of adapting to these demands and ensure precise output. To meet the quick deployment of applications/software, Automation Testing must be introduced to enterprise QA. With the adoption of the automation testing tool provided by repeato.app, the efficiency and agility of the software development life cycle are ensured and enterprises will be able to deliver quality software within a quick turnaround time. Repeato is a NoCode test automation tool for Android which works based on computer vision and machine learning. This way the tester does not need to care about the underlying architecture or framework in use, which simplifies the creation of tests.
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How Has COVID-19 Accelerated Digital Transformation
With more companies adopting remote work strategies and with everyone depending on applications and software to interact with each other, Digital Transformation has increased like never before. From e-learning and e-commerce to virtual reality (VR) and digital twins, newer applications are made continuously as well as monolithic apps are being considerably updated. As the speed of software development and deployment is continuously growing nowadays, there is a demand for quality and fast time-to-market testing. Since several of the testing responsibilities are too time-consuming to complete manually, enterprises are depending on automation testing.
What is Automation Testing?
Testing is considered the most significant phase in the software development life cycle (SDLC). The testing process ensures that errors are found and are accurately corrected, with the whole step repeating until no error has been found.
The major improvement in the testing process is Automation Testing, which is the application of software to carry out the entire process. Automation Testing is also capable of performing an in-depth and detailed comparison of actual test results with the expected results.
Automation Testing is significant nowadays because it is less time-consuming, as it saves the time of manual testing which can be quite laborious. In SDLC, Automation Testing occurs during the implementation as well as the testing phase. Automation Testing has taken over the manual testing process by reducing its need as well as by exposing the number of errors, shortfalls that cannot be acknowledged via the manual testing process.
Top Automation Testing Trends
Hyper-Testing is an agile and centralized strategy towards creating and executing a test strategy that implements end-to-end testing of all application layers. Hyper-Testing also allows non-functional elements by using the best-of-breed tools resulting in lower TCO and increased ROI.
Traditional testing strategies require a unilateral method of testing the application whereas Hyper-Testing delivers a holistic method of testing. Therefore, with Hyper-Testing every module of application/software is accurately and efficiently tested. It also identifies all the potential modules that can have a direct/indirect impact on the customer side experience.
Following are the key benefits of Hyper-Testing:
- 360˚ view of the application being tested
- Seamless coordination between distributed teams
- Access to comprehensive QA dashboard/metrics
- Shortened testing feedback cycle time by 40%
- Access to 1000+ devices/platforms in the cloud
- Slashed CoQ by 30%
- High ROI within 6 months
- AI-Based Test Scripts Design & Test Data Generation
Test Scripts Design
Creating automation test scripts is a fundamental phase in automation testing. Designing test scripts requires technical skills and this may not be easily performed by non-technical folks. Also, some of the key factors like re-usability, parameterization, test data management might not be addressed. Artificial Intelligence (AI) reduces these dependencies and will create automated scripts by examining predefined codes that are developed by QA analysts. The platforms use Natural Language Processing (NLP) to read the manual tests and auto-generate the scripts. In a nutshell, if the intent of the test step is right, the AI platforms generate automated scripts that can be run by any individual in the project teams.
Test Data Generation
Test data is another crucial element of testing, and therefore, it is essential to make sure that adequate and accurate data is available during testing processes. During certain tests, due to the lack of data sets for testing custom case scenarios, extensive testing of software is not performed effectively, consequently decreasing the quality of releases. This is normally because of the time consumption and effort demanded to generate such exhaustive data sets. With AI-based automation testing, the automated data generation helps testers create such data seamlessly from patterns provided by the users. Users can also decide to contribute seed data, and the AI will autonomously recognize the connections between various fields and generate additional data which will enhance the complete test coverage.
Codeless Automation Testing
Codeless Automation Testing is an idea/methodology recommended by test automation tools. It has a user-friendly GUI with provisions that enable test engineers to create test cases by simply choosing objects and adding operations to them. This is done by selecting the actions from a drop-down lookup list along with the provisioning of test data passed locally or externally, while making use of the codeless test automation tool. The end users do not have to program scripts for each test case and test steps in any scripting language. Hence, Codeless Test Automation becomes language agnostic.
Codeless Automation is expected to bridge all gaps that code-based test automation carries.
- Multiple browser support: for cross-browser testing
- Multiple database support
- Multiple file system support
- Data-driven testing support
- Mobility support
- Good and flexible reporting mechanisms
- Plug and play assets: offering the flexibility to plug and play test automation assets and calling already automated assets/components
While making use of the codeless test automation tool, the end users do not have to program scripts for each test case and test steps in any scripting language. Hence, Codeless Test Automation becomes language agnostic.
Test Automation Services are developing very quickly, innovative tools and technologies are appearing nearly every week, but they are not yet fully replaceable, and everything cannot be automated. Soon, there will be a trend of increasing the number of automated testing solutions and a gradual decrease in manual ones. It will become an obvious educational goal to become an expert in automation. After training in the practical use of the testing tools, manual testers will be able to perform automated testing. Therefore, every tester who wants to be more in demand in IT should learn at least one programming language. So learning programming and testing are processes that depend on each other. In the world of software testing, there will always be room for both manual and automatic testing. The choice of using the right strategy is to be made by the enterprise that needs it the most.
Ricky Philip is an industry expert and a professional writer working at ThinkPalm Technologies. He works with a focus on understanding the implications of new technologies such as artificial intelligence, big data, SDN/NFV, cloud analytics, and Internet of Things (IoT) services. He is also a contributor to several prominent online publishing platforms such as DZone, HubSpot and Hackernoon.