software testing trends

Leading 5 Software Testing Trends of 2020

The year 2020 too will mark the continuation of tremendous changes in technology and digital transformation, thereby requiring the organizations to constantly innovate and reinvent themselves.

Software development has undergone a massive transformation over the last decade.

The availability of new tools, technologies, and methodologies has made software development easier.

Here is the list of top 5 software testing trends in 2020.

Artificial Intelligence and Machine Learning:

Artificial Intelligence (AI) in software testing aims to make testing smarter and more efficient. AI and machine learning apply reasoning and problem solving to automate and improve testing.

AI in software testing helps reduce time-consuming manual testing, so teams can focus on more complex tasks, like creating innovative new features. Machine Learning (ML), in turn, is developed from the study of pattern recognition and computational learning approach in AI.

The main purpose is to make machines learn without being explicitly programmed. This science absorbs tons of complex data and identifies schemes that are predictive.

One of the most common and well-used ML-methods is Deep learning that is based on learning data representations. It is also based on neural networks in the human body.

DevOps and Agile:

DevOps is a software development method which focuses on communication, integration, and collaboration among IT professionals to enables rapid deployment of products.

It is a culture that promotes collaboration between Development and Operations Team. This allows deploying code to production faster and in an automated way.

It helps to increases an organization’s speed to deliver application and services. It can be defined as an alignment of development and IT operation.

Agile Methodology involves continuous iteration of development and testing in the SDLC process. This software development method emphasizes on iterative, incremental, and evolutionary development.

Agile development process breaks the product into smaller pieces and integrates them for final testing. It can be implemented in many ways, including scrum, kanban, scrum, XP, etc.

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.

Robotic Process Automation (RPA):

Robotic process automation (RPA) is a new engineering discipline that enables the product team to take on designing and implementing powerful digital solutions.

RPA systems develop a list of actions to automate a task by watching a user perform that task in the application’s GUI, and then repeating those tasks directly in the GUI.

Among the software teams that benefit from RPA, testers also come into the picture. To capture robots’ actions and simulate them for test purposes is no easy task, and RPA helps on several fronts.

At a simplistic level, it enables testing not just for robot-based solutions, but for any technology-powered product. For example, it could be a retail platform with a point-of-sale device connected to simulate an in-store transaction. If this has to be automated end to end, RPA is the key to simulating human actions in operating the device.

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 toolchains. 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.

Transition to cloud

Businesses are moving their data and processing to the cloud. 76% of all applications are cloud-based today. There is also an increased demand in industries adopting IoT.

Quality assurance for cloud-based installations requires specialised skills. The QA team needs to understand the app’s implications and business processes.

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