WebAPIRec: Recommending web APIs to software projects via personalized ranking

• Main Authors: THUNG, Ferdian, OENTARYO, Richard J., LO, David, TIAN, Yuan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: Personalized ranking; recommendation system; Web API; Computer and Systems Architecture; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/3912; https://ink.library.smu.edu.sg/context/sis_research/article/4914/viewcontent/07935481__1_.pdf
• Institution: Singapore Management University

Application programming interfaces (APIs) offer a plethora of functionalities for developers to reuse without reinventing the wheel. Identifying the appropriate APIs given a project requirement is critical for the success of a project, as many functionalities can be reused to achieve faster development. However, the massive number of APIs would often hinder the developers' ability to quickly find the right APIs. In this light, we propose a new, automated approach called WebAPIRec that takes as input a project profile and outputs a ranked list of web APIs that can be used to implement the project. At its heart, WebAPIRec employs a personalized ranking model that ranks web APIs specific (personalized) to a project. Based on the historical data of web API usages, WebAPIRec learns a model that minimizes the incorrect ordering of web APIs, i.e., when a used web API is ranked lower than an unused (or a not-yet-used) web API. We have evaluated our approach on a dataset comprising 9883 web APIs and 4315 web application projects from ProgrammableWeb with promising results. For 84.0% of the projects, WebAPIRec is able to successfully return correct APIs that are used to implement the projects in the top-five positions. This is substantially better than the recommendations provided by ProgrammableWeb's native search functionality. WebAPIRec also outperforms McMillan et al.'s application search engine and popularity-based recommendation.