We have witnessed continuous evolution in the software testing industry. With these developments in technology, software testing experts keep a close eye on the latest trends to ensure the success of software applications. Moving forward from the first quarter of 2020, we face new challenges, IoT devices are the most popular in these evolving times. The number of connected smart devices is expected to reach a total of 20.8 billion. With the rapid growth in the IT sector, the IoT market is also expected to grow exponentially, which will make software testing more challenging than ever.
Artificial Intelligence (AI) has made a remarkable difference to our lives in different spheres. However, due to rapid development in data collection, processing, and computing capabilities, AI has become the new norm for all businesses. In the coming years, AI is anticipated to unlock its true potential and improve efficiencies in software testing more than ever before.
With quality being at the forefront of all software products, customers are now more concerned about it. They expect fast releases without any compromise on their products. Thus, the reduced time to market also poses great challenges for the QA teams. Organizations need a focused and effective strategy to manage large QA teams in order to ensure customer satisfaction. With a well-defined framework in place for software quality and the right software testing tools, organizations can ensure that they are on the right track.
New Testing Trends and Techniques
Transition to CI/CD Approach
Organizations moving to DevOps practices, follow the continuous integration (CI) and continuous delivery (CD) approach. This means that QA teams deliver small chunks of software in fast iterations. With this testing technique, testers use the shift-left approach and begin testing earlier in the software development process.
It is also expected that QA teams move towards more test automation to ensure better test coverage while meeting the stringent release deadlines. Test automation depends on efforts from the development, testing, and operations teams. However, this does not eliminate the role of manual testing. Testing types such as exploratory and usability testing are to be performed manually.
ML & AI Testing
AI-powered software testing tools can also be used for the following:
Log Analytics – Identifying all test cases that need to be executed manually.
Traceability – AI can assist with extracting keywords to ensure maximum test coverage
Test Suite Optimization – To determine redundant test cases
Predictive Analysis – It forecasts the use of behaviors and identifies application areas to focus on based on these predictions.
Defect Analysis – Identifying high business risk areas
In the wake of the prevalent situation, it becomes highly critical for software testing companies to be equipped with innovative software testing tools. These tools should not only support the above-mentioned techniques including automation, CI/CD approaches, AI testing, etc. but also ensure the management of testing processes and collaboration between team members and important stakeholders.