The research firm Markets and Markets predicts that the AI market will grow to a $190 billion industry by 2025! If these stats are anything to go by, we can expect that smart devices will be a predominant factor impacting our lives in the near future!
As different sectors become aware of the benefits that AI has to offer, they are making a beeline to adopt AI and build it into their products. AI is making an invasion into highly sensitive sectors such as finance, healthcare, auto industry, etc. as well.
Just like other software algorithms, AI algorithms also warrant testing and quality assurance. This is because there is no scope for error when it comes to the safety of patients’ health, public safety, or a customer’s livelihood.
Testing of Artificial Intelligence is different from testing any other software. This is because AI systems need to satisfy quality characteristics such as performance, reliability, robustness, usability, and security, besides demonstrating ethical behavior. Only after satisfying these criteria, they can be deployed.
Dataset: It comprises the basic data that is input. It may be the organization’s historical data that need to be processed.
Training Data: It is the dataset that is used for training the AI model during the development stage.
Test Data: Test data is the dataset that is used to test if the model works as intended. Test data is different from the training data because the purpose of test data is to check if the model has learned from the training data and gives the desired results.
Model: It comprises of the algorithms from which the AI system learns.
Training Phase: It is the stage during which the model learns from data and makes the necessary predictions. The QA testing in the training phase entails ensuring that the algorithm and data while considering hyper parameter configuration data along with the associated metadata provide the predictive results that are expected. If the model fails at this stage, it needs to be rebuilt using better training data. This test is performed before the AI model is put into operation.
Inference Phase: It follows the training phase where the model can make inferences based on the input data. In the inference phase, the behavior of the model is checked with real-time data. The QA testing at this stage is done with a sample of the real world. Also, at this stage errors due to human bias are eliminated as far as possible.
Source Code: A machine learning program has less code than any other software program. However, it is likely to have errors. Therefore, unit tests and other tests are run on the source code.
Input and Output Values: Input and output values are the fundamental test objects in an ML algorithm. It is vital to verify the input values in an ML program because it is difficult to predict how the data will be processed.
The sensors of an AI system collect huge volumes of data. This creates unmanageable datasets that present problems in storage and analysis.
An AI system works by constantly learning. The challenge arises when there are unexpected events. In such a scenario, it becomes difficult to collate the data and train the system.
AI systems are trained and tested by humans and hence are vulnerable to human bias. Therefore, AI testing needs to test the system for human bias and eliminate it.
In an AI system, a single defect gets intensified to a large extent, making it difficult to identify the specific problem.
In order to overcome these challenges in AI testing, the testing needs to be approached in a systematic manner.
For any AI system to be successful, the input data should be free of any errors. The first step for AI testing is checking the data. The input data of an AI system needs to be scrubbed, cleaned, and validated. The quality assurance team should ensure that the input data is free of any kind of human bias or any kind of variation. This is because any flaw in the input data can lead to complications in the system’s interpretation of data leading to errors in the output. This could have serious repercussions, for instance, in a driverless car, it could lead to wrong navigation and even lead to accidents.
An AI system has an algorithm at its core. It is this algorithm that is responsible for processing data and generating insights. Some common examples of AI algorithms are the ability to learn, voice recognition, real-world sensor detection, etc.
The algorithm of the AI system should operate without any error. The algorithms should be tested again and again to ensure that they are free of errors because they can have grave consequences.
AI algorithms are tested using model validation, successful learn ability, the effectiveness of the algorithm, and a core understanding of the mind.
Performance and security testing are a vital aspect of testing of AI systems. Regulatory compliance is an essential part of the performance and security testing. Performance and security testing ensure error-free performance that has incorporated security measures to protect the system from cyber-attacks.
AI systems are generally a part of a complex network of applications. These diverse applications are integrated into a composite system and there is a possibility that the integrated system may include more than one AI system. Therefore, a holistic approach to testing is required. A system integration testing tests the entire system on various parameters when it works with conflicting goals.
Following use-cases must be tested to ensure the AI system works seamlessly:
An AI application needs to be tested for functionality and system levels. It is similar to testing of traditional software in aspects of test planning, test modeling, test design, and execution. However, it differs from traditional software in features such as non-oracles, timeliness, and the ability to learn. Therefore, testing of an AI system becomes more challenging and function test quality evaluation becomes an integral part of AI application testing. This test entails the testing of different quality attributes against pre-defined metrics.