With the ever-increasing popularity of microservice architecture, a considerable number of enterprises or organizations have encapsulated their complex business services into various lightweight functions as published them accessible APIs (Application Programming Interfaces). Through keyword search, a software developer could select a set of APIs from a massive number of candidates to implement the functions of a complex mashup, which reduces the development cost significantly. However, traditional keyword search methods for APIs often suffer from several critical issues such as functional compatibility and limited diversity in search results, which may lead to mashup creation failures and lower development productivity. To deal with these challenges, this paper designs DAWAR, a diversity-aware Web APIs recommendation approach that finds diversified and compatible APIs for mashup creation. Specifically, the APIs recommendation problem for mashup creating is modelled as a graph search problem that aims to find the minimal group Steiner trees in a correlation graph of APIs. DAWAR innovatively employs the determinantal point processes to diversify the recommended results. Empirical evaluation is performed on commonly-used real-world datasets, and the statistic results show that DAWAR is able to achieve significant improvements in terms of recommendation diversity, accuracy, and compatibility.

Integrating lightweight web application programming interfaces (APIs) into mobile Apps is a promising way for quick and cost-effective development of mobile Apps with desired functions. Web APIs, on the other hand, are created by distinct enterprises or organizations, making it challenging to develop compatible and diverse mobile Apps by combining existing web APIs. It has been demonstrated that this process is an NP-hard problem. In mobile Apps development, it is often necessary to read confidential information, leading to the business privacy leakage of enterprises. Thus, we devise a novel efficient web APIs recommendation (E-WAR) approach based on locality-sensitive hashing for recommending desirable web APIs to developers. Through analyzing industrial enterprises’ expected needs, E-WAR efficiently makes compatible and diverse web APIs recommendations while guaranteeing privacy protection. Finally, extensive experiments on real-world web APIs datasets are conducted. The results show that E-WAR can achieve significant performance improvements over the existing approaches.