Artificial Intelligence and Machine Learning Testing
With the advent of AI and Machine Learning, we are able to achieve maximum output with the least minimal amount of errors. Today, many organizations and structures have become self -reliant through their ability to run and test itself. Integrating AI to QA has remarkable benefits considering it helps in formulating smarter, innovative and faster testing architectures capable of adjusting to changes in its application. Due to such features of intelligent analytics, numerous organizations have already implemented them for their accurate predictions and interpreting decisions. Another favored testing aspect is the use of bots. Bots are used for generating test data, test environments, and lifecycle tests. Machine Learning is imperative as it plays a crucial role in real-time modeling transactions taking place in the banking domain and when integrated with AI can help trace illicit and suspicious transactions.
The primary intention of test automation has diverted from merely testing time to relying on the use of test cases. However, specific tools are directed to control test performances. Usually, the regression test, a kind of analysis which relies on monotonous activities is automated first. Reports for 2019 suggest that testers will use automation tools for testing both functional and non-functional tests.
IoT testing or Internet of Things test automation has gained significant momentum. IoT means that the internet interconnects numerous data. Those programs which are intended to be connected are required to be initially tested for their security, quality, and efficiency. Before any IoT testing process is carried out, there are precisely six elements of testing needed, which are compatibility testing, scalability testing, security testing, performance testing, usability testing, and data integrity testing.
Big Data Testing
Big Data testing is crucial for achieving data security and encryption. Reports suggest that the coming years will see a whopping 400 percent rise, the primary reason being that the expense of data storage will be on the lenient side. Today, however, testers are more concentrated on testing management and data storage capability of Big Data. However, prospective future will see a change in focus to reports and visualization.
The Agile Approach
Digital transformation is moving up the scale along with the agile methodology for business purposes. 2019 will see the agile approach match digital transformation methods with business needs. Integrating the agile methodology will enhance digital transformation strategies for producing favorable business results.
Top Software Testing Trends For 2019 Gregg C. Lawson