  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.
