Artificial intelligence is a new era of technology which has already entered into human day-to-day life e.g. Siri, Alexa voice interface, etc. The advancement of data storage through big data and data science has enabled the users with the quick analysis and data retrieval. Machine learning is the new field where machine enables the software to learn new things as it keeps on capturing data into the system. It is a continuous data feeding system that helps the machine to learn and behave according to the data pattern and heuristics. When it come to deal with the testing of Artificial intelligence system then the following 10 things need to be considered by the testing team.
1) Being A Tester, How Artificial Intelligence Testing Differs From Traditional Software Testing?
No doubt a system with Artificial intelligence is nothing less than a software but such software has a capability to learn and change its behaviour accordingly. A traditional software functional testing involves the testing of expected output versus actual output received for the given set of inputs. In the case of Article intelligence, the expect output may vary as machine learning progresses over the period of time. Therefore, the artificial intelligence system testing differs from traditional software testing and the testers should be ready to absorb such kind of testing procedures for AI.
2) What Are Testing Outputs For AI System?
As explained before, the outputs in artificial intelligence are not always going to be the same for the same set of inputs. Therefore, it is very important to understand the underlying machine learning model of the system and update the test cases accordingly. The tester should understand how the machine is going to learn and behave when the data is fed into the system. The tester should able to test the behaviour of the AI system in the both positive as well as negative scenarios.
3) Consider The Legal and Ethical Requirements For Testing AI System
AI system may lead to develop the lethal robotic system that may be dangerous to the mankind. This is where the legal, ethical, and societal requirements should be completely reviewed before designing the test cases and scenarios. It is the responsibility of the testers to test those aspects of the AI system by conducting negative tests which could lead to legal, ethical, and societal issues. Therefore, AI system testing is very crucial and should be conducted very carefully to make sure we are building machines which are useful to the mankind.
4) AI Algorithm Testing
In Artificial intelligence, the machine analyses the data pattern and use these patterns to build the rules also known as Heuristics. This is the similar operation that the human brain does and helps the human to make a decision. The machine after loading the data and analyzing the pattern it becomes aware about the pattern and keep itself updating about the patterns as it receives. Therefore, the behaviour of the machine and its AI algorithm are not going to behave the same always and therefore we cannot expect the output going be 100% perfect all the time. It depends on the heuristics that machine has developed over the period of time based on the data pattern.
5) Test Data To Test AI Algorithms
Machine learning is the part of Artificial intelligence where the machine builds its knowledge based on the type of data and its pattern fed to it. The tester should carefully choose their test data to test machine learning such that the pattern of data they are feeding to the machine should match the expected output from the AI system in terms of the pattern being fed to the AI system.
6) AI Architecture and Design
The AI system tester should have ample knowledge of the AI architecture as well as the system design. This is because it will help the tester to design its test cases after knowing the fact that output is going to change with the change in the input data pattern to the AI system. Therefore, testing of the AI system as a black box should be involved with the knowledge of the architecture and the system design.
7) Rigorous Testing Is Required For System
AI system do not behave like the traditional software as they have the capability to learn as they are fed data. Therefore, in order to do the deep analysis of the AI system it is important to perform the rigorous testing after feeding the data in bulk with distinct patterns. More meaningful data is fed to the machines, it will develop heuristics and machine can perform decision making and this is where the tester can test the machine behaviour accurately.
8) Same Input But Different Output Than Before
This is understood behaviour of the AI system and the test cases cannot be declared as failed as the actual functional testing should be based on the pattern, rules, and heuristics but not against the behaviour for the same set of inputs. In Artificial intelligence machine learns and changes its behaviour accordingly.
9) Outputs Management Are Critical
In AI, the AI algorithm could be improved dynamically with the data pattern fed to it. The testing mechanism can be taken as an opportunity to train the AI system and study its impact immediately. A well trained machine i.e. improved algorithm can be directly used for the production. But, we need to ensure that the machine has the required knowledge of the purpose it has been built for.
10) Regression testing
The data set can be used for the regression test where machine pattern was tested before. But repeating the test case may not reap the same result over and over. Therefore, the regression testing may not be that productive unless it is done for a net new AI machine. The regression testing should be based on the testing of the AI algorithm behaviour against the set of data being fed to the system.
Conclusion
AI testing is different from the traditional software testing and the testers should take into consideration the above points before testing any AI system.
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