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The Software Testing Trends to Watch For in 2021

Modern customers demand a seamless software experience in this rapidly evolving world. Enterprises are under tremendous pressure to deliver customer-centric, competitive solutions in order to meet this increasing demand. Global businesses are beginning to recognize the importance of software testing and quality control (QA) early in the software development process. It is the easiest and most cost-effective method to ensure faster software release cycles, reduce time-to-market, deliver high-quality software products, and provide low-cost software products. With the advent of next-generation technologies like artificial intelligence, cloud computing, and robotic process automation, as well as mobility and DevOps, companies are facing new challenges in software testing and development. We have highlighted recent trends in software testing to help you deal with the current challenges and realities of software production.



Machine learning and artificial intelligence are gaining popularity in testing





Software teams can improve their testing strategies and accelerate their release cycles by leveraging AI. Future plans call for AI to be utilized in more areas of analytics and reporting.


  • AI algorithms are used to identify test cases that require both manual and automatic testing.
  • Optimize test suites by eliminating unnecessary test cases
  • To ensure optimal coverage of the test to identify keywords from a Requirements Traceability Matrix
  • Predicting key parameters, metrics that define end-user behaviour and identifying areas of focus;
  • Detecting application areas and defects that could be associated with business risk.

Automation tools for testing are also being created using AI. These tools will allow QA teams to create tests from scratch, identify issues and minimize human intervention during testing generation and maintenance. Machine learning is also a key component of automation. Complex neural networks and algorithms are capable of predicting certain outcomes. Applications require continuous validation and testing, so we can expect analytics-centric solutions using ML to gain some traction in solving potential issues.



Internet of things (IoT), testing to increase digitally connected smart devices




The IoT is a rapidly growing technology concept. It's a combination of huge networks, including devices, people, processes, and technologies that collect and share data over the internet. Gartner's research shows that there will be 25 million internet-connected devices in 2021, as opposed to the 14.2 billion devices of 2019. These numbers clearly show the growing interest in IoT, particularly in healthcare. Effective IoT testing strategies are required because IoT devices include software and IoT devices are increasingly connecting to generate large amounts of data. This will be necessary for the coming years to secure the software against vulnerabilities and threats and protect data security. There will be many testing combinations needed to test new devices, communication protocols, and operating systems. Software testers will be in high demand to test and monitor for delays, security, data integrity, performance. IoT testing will require the QA teams to increase their knowledge. End-users will have well-connected, secure, and efficient smart devices.


Integration of QA automation





As more businesses adopt agile and DevOps processes to meet the demands for speed and quality, test automation services are becoming a critical component of their testing. In 2019-2020, 44% of IT companies automated 50% of their testing. The global automation testing market is expected to grow to USD 68 billion in 2025. Automated testing allows teams to automate repetitive tasks, find bugs faster and more accurately, give continuous feedback and cover all aspects of the test. Automated testing can help organizations save time, money, and human resources. This is a list of top trends in automation testing that will impact the future of software testing.


  • Automated codeless testing: These tools can be used to automate testing scenarios using AI and visual modelling.
  • Automation testing using robotic process: this includes automation testing tools that aid testers in replacing regression and load testing. It's especially useful when dealing with large and complex data sets.
  • A mix of agile and DevOps: This type of automation testing is great for agile teams and helps businesses to deploy faster and manage costs.

Performance testing is being replaced by performance engineering.





Performance testing and engineering are essential due to the increasing demand for high-performance and fast-loading applications. Performance testing can only identify performance bottlenecks after development is complete. Therefore, the trend is moving towards performance engineering where QA teams design applications from the beginning of software development. Performance issues should be addressed early in the software development process. This will save you a lot of time later on, and reduce the need to troubleshoot and rewrite. Performance testing focuses on many elements such as software, hardware, performance configuration, security, and usability. It is essential for meeting rapidly evolving requirements, short development cycles, and frequent releases of applications.

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