5 Test Automation Trends To Watch Out For In 2021

Automation Testing Trends in the next five years

Today, the IT world is witnessing an increase in the management of infrastructure and operations (I&O) tasks using automated technologies. Post pandemic, the enterprise IT industry evolution has spiked. Business organizations are fast addressing these changes by implementing novel technologies, practices, solutions and latest test automation trends.

Some test automation trends are being predicted to create waves in 2021. Available statistics around them include:

  • A huge demand for Hybrid IT and infrastructure related to multi-cloud capabilities. The prediction says that over 75% of organizations would opt for the same in 2021.
  • About 75% of the databases would be cloud-based.
  • Off-site cloud environments would see only 50% of enterprise generated data being created and processed.
  • To facilitate agile and scalable IT ops, 40% I&O teams adopt AI capabilities.
  • 90% of organizations would have dedicated IT automation by 2025.
  • Hyper-automation is slated to reduce IT operations cost by 30%.
  • Motivation to drive innovation slated to result in the reduction of hybrid IT environment complexity.

Test automation services are already an important part of the quality engineering industry. Availability of diverse testing tools to automate the different types of tests is enabling quality even in shortened development cycles. However, the primary focus of adopting these test automation latest trends is to:

  • Minimise manual testing
  • Increase accuracy and cost-effectiveness

Possibilities of the integration of CI/CD with an end-to-end solution of the hybrid framework are yet to be explored. Formulating new technologies to find out more and change the business landscape for the better by facilitating enhanced QA has resulted in 5 important thoughts that are currently trending. They are:

1. Vulnerability Prediction

Would it not be nice if you could predict the location of a bug? Annihilation processes could be adopted faster and information loss, deadlock or even system failures could be reduced. An intelligent system should have this capability. This need for an accurate vulnerability prediction is shaping the approach of new vulnerability models. The challenge comes:

  • By way of capturing both syntactic and semantic source code representation sufficiently and
  • In the form of an increase in robustness and complexities of software developed.

Capturing vulnerability is like finding a needle in a proverbial haystack. Static analysis tools used, generate false positives while dynamic analysis tools need:

  • Detailed run-time property monitoring and
  • A diverse range of test cases.

The solution lies in the use of ML to generate an effective collaboration of test automation services and software developers. Bug data from previously released versions also helps as it contains information of importance like bug-type, priority, and severity.

Analysis of this information helps developers proactively use automated testing during the testing cycle in these potentially vulnerable software codes.

2. Parallel  Execution with Autoscaling CI/CD Runner

Traditionally a monolithic app is dependent on a single-build pipeline for its application executable output. The presence and detection of a high-priority bug in such a system warrants immediate fix integration, testing and then publishing thereby delaying their release. This can be mitigated using featured branches and well-factored modules that minimize code change impact but these complexities will also make the monolithic app brittle enough to break.

Alternatively, when using microservices, a product becomes a combination of several different applications, each having its own CI/CD pipeline and release to production. Thus herein, several small focussed services an app or task with each service being independent of the others and running parallel. They can, thus, be deployed and tested individually. This concept of parallel execution helps to maximize test automation benefits by reducing execution time and fast-tracking test cycles.

Additionally, depending on the number of tests that need to be run, the gridscaler hub would immediately autoscale to the number of nodes required and enable their attachment to the hub. After the completion of a test run, these nodes can be terminated and the cycle can continue uninterrupted.

But for the optimal consumption of test resources, the autoscaling runners need to auto-manage the server ups-and down. This will help manage queues when parallel testing is enabled. Of course, the DevOps team has to build CI/CD runners that are scalable and robust to achieve this.

3. Test Scenario Automation

Test script generation is a cumbersome and time-consuming process. Rapid development and deployment of software is the need of the hour. Hence, test scenario creation needs to be fast, automated and without human intervention. Better collaboration between testers and developers will also result in faster test scenario generation. Some practices that are slated to help the same in 2021 are:

  • Better collaboration between the popular DevOps and Agile technologies; once integrated they will complement the other resulting in quality products in super-quick time.
  • Using AI and visual modelling to enable codeless test automation facilitates faster, easy to review and efficient test case generation that requires a low learning curve and lesser resource.
  • Integrating AI and Ml to facilitate:
      • Building of automated test suites requiring very little or no human intervention,
      • Predictive analytics,
      • IoT testing adoption to enable compatible, easy to use, practical, and secure test automation services
      • Extensive implementation of RPA etc.

4. UI Object Locator Diagnosis and Fixing

This is a big requirement for an automation team. Objects and their concerned properties keep changing frequently during the Agile software development cycle. Automation systems that are currently in use are not smart enough. They cannot understand the difference between an upgrade, enhancement, a change and a potential bug. Some common UI identifier issues faced are:

  • Element not found,
  • Disabled or hidden elements,
  • Duplicate UI identifiers,
  • Empty UI identifiers,
  • Stale element etc.

This challenge needs complete annihilation by:

  • Making smart testing scenarios,
  • Enabling the system to recognize the object hierarchy,
  • Adapting the system for test automation generation and modification,
  • Raise changes in UI without halting test execution,
  • Enabling system training so that it can:
    • Identify new object locators,
    • Detect changes in existing locators,
    • Self-generate test scripts to absorb addition, deletion and modification of objects.

This will enable faster product deployment.

5. Implementation of DevTestOps

This concept was formed by the amalgamation of Continuous Testing and DevOps. The popularity gained by this concept is because it helps in the development of a flawless end product seamlessly and in a reliable manner. Embracing the DevTestOps concept also helps accelerate app development and deployment lifecycle, thereby assisting Agile teams to make frequent and quick app releases.

The year 2021 is slated to see major software updates across fields. Thus immediate bug detection during the development of these updates will attain greater importance. This will further warrant that the testing and QA teams work closely with the developers. The objective would be to enhance the software development process to finish quickly and deliver a superlative app as soon as possible.

About the Author

QA InfoTech

QA InfoTech

Established in 2003, with less than five testing experts, QA InfoTech has grown leaps and bounds with three QA Centers of Excellence globally; two of which are located in the hub of IT activity in India, Noida, and the other, our affiliate QA InfoTech Inc Michigan USA. In 2010 and 2011, QA InfoTech has been ranked in the top 100 places to work for in India.

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