10 Software Testing Trends to Watch Out For in 2019

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.

Software Testing Trends

Software testing is a continuously evolving field as new technologies require companies to be innovative and up to date with current trends. Here are the current trends in software testing.

Test Automation is a technique that has grown massively over the last few years. While it doesn’t cover all bases, it is very useful for finding defects and bugs in the basic functions of the software. It also saves testers a lot of time by not having to run all the tests themselves.

Agile and DevOps are methods that are very useful when dealing with changing requirements and the need for efficiency. The adoption of both methods helps to reduce the lifecycle of the project, while still delivering quality results.

Artificial Intelligence (AI) is a fast-growing innovation that allows for smarter automated testing. AI will allow for fast adaptation to changes, optimization of automated testing, and more efficient testing.

The Quality Assurance professionals have to rapidly adapt to the changes in the software testing industry to stay relevant. Here is a list of 10 software testing trends to watch out for in 2019:

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 optimization – 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 prioritization 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 technology 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 system

Reliability & Scalability Testing – Simulation of sensors utilizing virtualization 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

6. Performance Engineering

Performance engineering will replace performance testing in 2019.  Rather than executing performance test scripts, the focus will be on analyzing how all the elements of the system work together.  The various elements of the system include performance, security, usability, hardware, software, configuration, business value and the customer.  The performance engineering is all about collaborating and iterating on the items of highest value and delivering these items quickly to ensure a high-quality product. Performance engineering will help in exceeding customer expectations in 2019.

7. Test Automation

The test automation helps the testing team to focus their time and efforts on creating test cases rather than managing testing needs. Test automation helps to track and manage all testing needs, the types of testing required along with the test coverage. Test automation helps to ensure high- quality software is delivered.

Specialized tools are used to control the execution of tests to compare the actual results against the expected results. Mainly the regression tests which require repetitive actions are automated.  Automation tools are used for both functional and non-functional testing.

8. Combining Manual and Automated Testing

More and more quality assurance professionals are using a combination of manual and automated testing approaches to harness the benefits of both as well as to overcome their respective shortcomings. The manual testing is still dominating the testing industry. Although automated testing brings about efficiency in the testing process there are still certain areas such as usability and design which requires manual efforts.

9. Shortening Delivery Cycle

Rapid changes in technologies, platforms, and devices are putting pressure on software development teams to deliver the finished products faster and more frequently. Testing needs to be integrated with development to facilitate delivery. Software organizations are ready to invest more in improving their development and delivery processes by employing the right set of tools. The need for test management tools will increase to match up with shortening delivery cycles.

10. Integration

With shortening delivery cycles, there is a greater need for integration of various elements of a product development. To facilitate smart testing and analytics, the data is collected from disparate sources such as requirement management systems, change control systems, task management systems, and test environment. This implicates that there is a need for integrated tools which help in requirement management, task management, bug tracking and test management. The integrated tool act as a repository of data of different modules that can be shared and used.

Conclusion

To stay ahead of the curve, the QA professionals’ needs to stay updated on the latest testing trends. The only way to be immune to the disruptions in the software industry is to prepare for the future. The above-mentioned software trends will help testers in investing their time and efforts in acquiring the rights skills and tools for 2019.