Innovating API Recommendations: A Comparative Analysis of Machine Learning Techniques [Video]

In the ever-evolving landscape of software development, APIs (Application Programming Interfaces) have become essential tools for developers. With the exponential growth of available APIs, finding the right one for a specific project has become daunting. This challenge has spurred the development of API recommendation systems powered by machine learning techniques. Anusha Kondam delves into these innovations in her comparative evaluation of different machine-learning approaches.

The Exponential Growth of APIs

The rise of APIs has been nothing short of spectacular, growing from just 105 public APIs in 2005 to over 22,000 by 2019. This growth, while beneficial, has made it increasingly difficult for developers to locate the most suitable APIs for their needs. Traditional methods of API discovery are often time-consuming and inefficient, leading to significant productivity losses. As a response, machine learning-based API recommendation systems have emerged, offering personalized suggestions based on user preferences, project requirements, and contextual factors.

Collaborative Filtering: Leveraging User Interactions

Collaborative filtering plays …