DAOR: Distinguish Similar Machine Learning APIs Based on Official Documents and Reviews

Background In recent years, machine learning (ML) APIs have emerged as a valuable resource for addressing complex problems, such as image recognition. However, developers should use ML APIs carefully, as they have their own characteristics different from traditional ones: an ML API has its own training data set, a concrete target task, and its output is often the probability. As a result, developers may use an inappropriate API, and the program can still run without reporting errors, especially as there are many similar ML APIs provided by different platforms. Methods This paper proposes an approach called DAOR to help developers use ML APIs properly in their tasks. First, a comparative analysis of ML APIs is conducted, leveraging information from documentation and user reviews to identify comparable APIs. This involves extracting differences from the documentation, categorized into inputs, functions, and outputs, and summarizing key information from user reviews using GPT‐driven prompts. Finally, a visualization framework is designed to summarize and show the results. Evaluation and Results To evaluate the approach, a series of experiments is conducted based on the ML APIs from two famous platforms, Amazon Web Service AI and IBM Watson. The results show that useful information for distinguishing similar ML APIs can be gained, and it is helpful for developers to use the ML APIs correctly.