Trends for software testing that you need to pay attention to 2021

 

1. Automatic testing without character

The soil code test automation tool is built on artificial intelligence technology and visual modeling, enabling the acceleration of the formation of test cases that meet testing automation. Using extraordinary automatic testing tools, QA engineers can create a test case scenario with zero coding knowledge and reduce the time spent on recurring test cases. Increased adoption of automatic test equipment without code will be one of the software testing trends that you need to pay attention to 2021.

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Some advantages of automatic testing without characters as below:

 

Simple to review: Because this test case is produced without any code, it is clear and can be read for people who do not understand the code method. Therefore, the case of the test can be easily reviewed by even non-technical stakeholders in the project.

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Low learning curve: with automatic typical testing, test cases can be produced even while users have completely no familiarity with programming or coding languages. Therefore, it does not need extra time and efforts to learn and start building test cases.

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Save valuable resources: with automatic tests without character, QA engineers do not require learning new programming languages ​​and do not require new people to be employed for coding skills. Therefore, resources, costs, and time can be easily saved easily.

Effective: Because the learning curve is stable and slow, and the generation of test cases does not require complicated syntax, the formation of rapid test cases and accelerating the effectiveness of the automation process as a whole.

Learn more about the automation test without code and why is that the future?

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2. Machine and Artificial Intelligence Learning for Automation

Helploed that the use of AI will continue to grow only with every aspect of creative technology because the more applications we use on interconnected planets. The current investment in artificial intelligence is anticipated is USD 6-7 billion in North America. In 2025, artificial intelligence overall global investment is expected to reach almost USD 200 billion.

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We will hope to watch artificial intelligence applications in more testing zones - which will mostly apply to analytics and reports:

 

Optimizing the Suite test: determine and eradicate unnecessary and redundant test cases.

Log Analytics: Find extraordinary test cases that require manual & automatic tests.

Defection Analytics: detect application areas and defects that bind the risk of the company.

Predictive analysis: Estimate the main parameters and customer final behavior specifications and find the application area to concentrate.

Confirming the coverage of the test requirement: taking important keywords from RTM (TRACEABLISTION MATRIX requirements).

Testing of software and the QA team can utilize engine learning (ML) and artificial intelligence (AI) to improve their automated test strategies and offset repeated releases - with analytical and reporting assistance. For example, software testers can use the AI ​​algorithm to find and prioritize the scope for additional automatic testing. In addition to sorting out the workload of software tests, AI-powered test applications can optimize the test suite after identifying test cases that are not needed and ensure optimal test coverage by checking keywords from RTM.

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Machine Learning: If it can be tested, it can be taught

 

The pillar where smart automation rest is ML. According to the quality of the world quality Capgemini, 38 percent of companies have planned to execute machine learning projects in 2019. Business experts estimate that this number will increase in the coming year. Although projects the pattern of end-customer behavior is still a difficult job for human intelligence, analytic thief who supports machine learning can strengthen human intelligence by detecting less bright parts in the application. This insight can be used to predict the possibility of user behavioral parameters using historical data that can be accessed.