Key Considerations for Testing AI Applications
Artificial Intelligence has become a thriving technology, finding applications in various domains today. This article sheds light on fundamental ideas that testers should bear in mind while testing AI applications. Prior to reading further, familiarize yourself with the fundamental key terms in Artificial Intelligence:
Training Data constitutes an extensive dataset employed to instruct Machine Learning Algorithms utilized in AI, enabling them to make well-informed decisions. For instance, in the development of an image recognition application, the training data would encompass billions of images in diverse formats, each associated with different labels.
Biased Data refers to information within a dataset that is inaccurate and fails to accurately represent the complete details.
Below are some key ideas for AI testing:
- The tester's awareness of test data plays a vital role in effectively testing various scenarios of the AI application. Understanding the high-level structure of the training data relevant to the application is crucial in identifying multiple test data patterns.
- In AI applications, extensive training data sets are utilized by machine learning algorithms to execute numerous logical and arithmetic calculations, enabling them to arrive at conclusive decisions and deliver results to the user.
- When commencing the testing process for an AI app, the tester should thoroughly analyze the training data to determine its structure and design. Adopting this approach facilitates the creation of diverse test data patterns for each test case, thereby enabling a comprehensive validation of the application.
- In the scenario of testing an image recognizing AI app, the Training Data comprises billions of images, including thousands of images depicting cats stored in the database. The tester should craft test cases using various patterns of cat images as test data to thoroughly evaluate the AI app's recognition capabilities.
- The tester must also consider designing biased test data, as this approach aids in conducting search operations and generating least accurate positive search results. For instance, in an Image Recognizing application, uploading an image of a cat with only the eyes visible and the rest of the face blurred serves as an example of biased data.
- Furthermore, the tester should adopt a mindset of enhancing the training data by incorporating highly peculiar or biased test data. This practice can cause the algorithm to produce results such as "no search found" or incorrect outcomes. Subsequently, the algorithm can assimilate the peculiar test data into the training data suite, refining its performance over time.
- Certain AI applications make predictions by analyzing a set of input values. For instance, an AI app can forecast the future growth of a business by examining its past 5 years' history. To effectively test such predictive capabilities, the tester should create realistic test data that includes scenarios of consistent upward trends, consistent downtrends, or a mixture of upward and downward business trends.
- Test data should also be formulated with the mindset that the system might produce incorrect results when the input is valid, resembling the concept of 'False Positive'. Similarly, the system should generate a set of valid results when the input is invalid, instead of simply stating "your search operation has no valid result," resembling the concept of 'False Negative.'
