Top Software Testing Trends In 2024

Watch out the Top Software Testing Trends that one should anticipate in the year 2024.

👉 Agile and DevOps

• Organizations have embraced Agile as a response to rapidly changing requirements and DevOps as a response to the demand for speed.

• DevOps involves practices, rules, processes, and tools that help to integrate development and operation activities to reduce the time from development to operations. DevOps has become a widely accepted solution for organizations which are looking at ways to shorten the software life cycles from development to delivery and operation.

• The adoption of both Agile and DevOps helps the teams to develop and deliver quality software faster, which in turn is also known as “Quality of Speed”. This adoption has gained much interest over the past five years and continues to intensify in the coming years too.

👉 Test Automation

• In order to implement DevOps practices effectively, software teams cannot ignore test automation as it is an essential element of the DevOps process.

• They need to find the opportunities to replace manual testing with automated testing. As test automation is considered to be an important bottleneck of DevOps, at a minimum, most regression testing should be automated.

• Given the popularity of DevOps and the fact that test automation is underutilized, with less than 20% of testing is automated, there is a lot of room to increase the adoption of test automation in organizations. More advanced methods and tools should emerge to allow better utilization of test automation in projects.

• Existing popular automation tools such as Selenium, Katalon, and TestComplete continue to evolve with new features that make automation much easier and more effective too.

👉 API and Services Test Automation

• Decoupling the client and server is a current trend in designing both Web and mobile applications.
• API and services are reused in more than one application or component. These changes, in turn, require the teams to test API and services independent from the application using them.
• When API and services are used across client applications and components, testing them is more effective and efficient than testing the client. The trend is that the need for API and services test automation continues to increase, possibly outpacing that of the functionality used by the end-users on user interfaces.

👉 Artificial Intelligence for Testing

• Although applying the artificial intelligence and machine learning (AI/ML) approaches to address the challenges in software testing is not new in the software research community, the recent advancements in AI/ML with a large amount of data available pose new opportunities to apply AI/ML in testing.

• However, the application of AI/ML in testing is still in the early stages. Organizations will find ways to optimize their testing practices in AI/ML.

• AI/ML algorithms are developed to generate better test cases, test scripts, test data, and reports. Predictive models would help to make decisions about where, what, and when to test. Smart analytics and visualization support the teams to detect faults, to understand test coverage, areas of high risk, etc.

• We hope to see more applications of AI/ML in addressing problems such as quality prediction, test case prioritization, fault classification and assignment in the upcoming years.

👉 Mobile Test Automation

• The trend of mobile app development continues to grow as mobile devices are increasingly more capable.

• To fully support DevOps, mobile test automation must be a part of DevOps tool-chains. However, the current utilization of mobile test automation is very low, partly due to the lack of methods and tools.

• The trend of automated testing for mobile app continues to increase. This trend is driven by the need to shorten time-to-market and more advanced methods and tools for mobile test automation.

• The integration between cloud-based mobile device labs like Kobiton and test automation tools like Katalon may help in bringing mobile automation to the next level.

👉 Test Environments and Data

• The rapid growth of the Internet of Things (IoT) means more software systems are operating in numerous different environments. This places a challenge for the testing teams to ensure the right level of test coverage. Indeed, the lack of test environments and data is a top challenge when applying to test in agile projects.

• We will see growth in offering and using cloud-based and containerized test environments. The application of AI/ML to generate test data and the growth of data projects are some solutions for the lack of test data.

👉 Integration of Tools and Activities

• It is hard to use any testing tool that is not integrated with the other tools for application lifecycle management. Software teams need to integrate the tools used for all development phases and activities so that multi-source data can be gathered to apply AI/ML approaches effectively.

• For Example, using AI/ML to detect where to focus testing on, needs not only data from the testing phase but also from the requirements, design, and implementation phases.

• Along with the trends of increasing transformation toward DevOps, test automation, and AI/ML, we will see testing tools that allow integration with the other tools and activities in ALM.