Diversity-aware Web APIs Recommendation with Compatibility Guarantee

TL;DR

This paper introduces DivCAR, a novel approach for recommending diverse and compatible web APIs by employing graph sampling and Steiner tree algorithms, effectively balancing diversity and compatibility in API selection.

Contribution

DivCAR is the first method to combine diversity-aware sampling with compatibility modeling for web API recommendation using Steiner tree optimization.

Findings

DivCAR outperforms baseline methods in recommendation quality.

Experimental results show high efficiency and effectiveness.

The approach balances diversity and compatibility successfully.

Abstract

With the ever-increasing prevalence of web APIs (Application Programming Interfaces) in enabling smart software developments, finding and composing a list of existing web APIs that can corporately fulfil the software developers' functional needs have become a promising way to develop a successful mobile app, economically and conveniently. However, the big volume and diversity of candidate web APIs put additional burden on the app developers' web APIs selection decision-makings, since it is often a challenging task to simultaneously guarantee the diversity and compatibility of the finally selected a set of web APIs. Considering this challenge, a Diversity-aware and Compatibility-driven web APIs Recommendation approach, namely DivCAR, is put forward in this paper. First, to achieve diversity, DivCAR employs random walk sampling technique on a pre-built correlation graph to generate diverse correlation subgraphs. Afterwards, with the diverse correlation subgraphs, we model the compatible web APIs recommendation problem to be a minimum group Steiner tree search problem. Through solving the minimum group Steiner tree search problem, manifold sets of compatible and diverse web APIs ranked are returned to the app developers. At last, we design and enact a set of experiments on a real-world dataset crawled from www.programmableWeb.com. Experimental results validate the effectiveness and efficiency of our proposed DivCAR approach in balancing the web APIs recommendation diversity and compatibility.