Quality assurance plays a very vital role in software development. The functionality and quality of software rely largely on QA.
Quality assurance use tools to perform tasks. Thus, delivering satisfactory results and generating products of the best quality. Let us take a look at the tools to watch out for in quality assurance for 2019.
Testing team can focus their time and efforts on creating test cases rather than managing testing needs with test automation. It helps to track and manage all testing needs. Also, the types of testing required along with the test coverage.
Internet of Things (IoT) technology is gaining traction, connecting more devices than ever. IoT testing is conducted to test IoT technology based devices. The various types of testing for IoT systems are as follows:
- Usability Testing
- Compatibility Testing
- Reliability & Scalability Testing
- Data Integrity Testing
- Security testing
- Performance Testing
Digital Transformation With Agile
Data has become valuable in gaining insights in businesses undergoing digital transformation. The adoption of agile methodology for digital transformation is the latest addition to this trend. Furthermore, agile methodology aligns digital transformation initiatives with business needs.
Agile teams define challenges, objectives and use cases. In addition, new features are delivered incrementally with each sprint in the agile approach. Furthermore, agile helps to deliver valuable outcomes frequently for the business rather than waiting for a long time. Thus, the ongoing process of digital transformation makes this possible.
Machine learning is brings revolutionary changes in workflows and processes. Also, 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.
Big Data Testing
Big Data refer to the high volume of data generated at a high velocity. Therefore, testers must verify that terabytes of data are successfully processed. Hence by using commodity cluster and other supportive components. In other words, Big Data testing focuses on performance testing and functional testing.
Big Data testing is critical about data quality. Therefore, verification of data quality is done before the testing begins. Hence, the quality of data is checked on the basis of various characteristics. For instance, conformity, accuracy, consistency, validity, duplication, data completeness, and many more.
Other QA Tools To Watch Out For
Other quality assurance tools to watch out for are:
- Increasing Adoption of DevOps
- Performance Engineering
- Combining Manual and Automated Testing
- Shortening Delivery Cycle
Innovation is an inevitable part of the ever changing world of technology. Therefore, it will be best if we embrace and accept it. Primarily, the goal of technology is to improve the world, in general. Hence, there is no harm if we give technology the benefit of the doubt. Moreover, it has proven its worth in more ways than one.