Testing Trends to Watch Out for in 2020 – Part I

By 28 August 2019 August 28th, 2019 No Comments

Testing and QA professionals need to keep ahead of developing trends. See where software testing is headed in the next year to stay one step ahead.

With the advent of Agile and DevOps development technologies, the software development industry is undergoing major disruptions. This has lead to the evolution of new testing approaches. Quality Assurance professionals have to rapidly adapt to the changes in the software testing industry to stay relevant. Here is a list of at least five software testing trends to watch out for in 2020:

1. Digital Transformation With Agile

Businesses are undergoing digital transformation ever since data has become valuable in gaining insights. The latest addition to this trend is the adoption of agile methodology to undergo a digital transformation. Agile methodology helps to align digital transformation initiatives with business needs.
The agile team defines the business challenges, objectives and use cases. In the agile approach, new features are delivered incrementally with each sprint. As digital transformation is an ongoing process, agile helps to deliver valuable outcomes frequently for the business rather than waiting for a long time.

2. Machine Learning in Testing

Machine learning is bringing about revolutionary changes in workflows and processes. In testing, machine learning can be used for:

  • Test suite optimisation – To identify redundant and unique test cases.
  • Predictive analytics – To predict the key parameters of software testing processes on the basis of historical data.
  • Log analytics – To identify the tests cases which need to be executed automatically.
  • Traceability – Extracting keywords from the Requirements Traceability Matrix (RTM) to achieve test coverage.
  • Defect analytics – To identify high-risk areas of the application for the prioritisation of regression test cases.

3. Increasing Adoption of DevOps

In DevOps, the testing begins at the beginning of the development cycle. This development approach facilitates Continuous Integration and Continues Delivery. This allows testers to perform Continuous Testing and Continuous Monitoring to validate that the developers have built the right application. The functionality and performance of the application are tested continuously along with development.
The testing team aligns the test design, test automation, and test case development with DevOps to not only verify the code changes but ensure that the changes don’t break the product.

4. Big Data Testing

Big Data is the high volume of data generated at a high velocity. In Big Data testing, testers have to verify that terabytes of data are successfully processed using commodity cluster and other supportive components. This type of testing focuses on performance testing and functional testing.
The quality of data is also a critical factor in big data testing. The quality of data is verified before the testing begins. The data quality is checked on the basis of various characteristics such as conformity, accuracy, consistency, validity, duplication, data completeness, etc.

5. IoT Testing

There are more connected devices than ever before as IoT (Internet of Things) technology is gaining traction. IoT testing is conducted to test IoT technology based devices. The various types of testing for IoT systems are as follows:

  • Usability Testing – To test the usability of an IoT system
  • Compatibility Testing – To check the compatibility of devices in IoT systems
  • Reliability & Scalability Testing – Simulation of sensors utilising virtualisation tools
  • Data Integrity Testing – To validate the integrity of data
  • Security testing – To validate the user authentication process and data privacy controls
  • Performance Testing – To test the performance of the connected devices in an IoT network

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