Web-based Application Programming Interface (API) has become an important tool for modern software development. Many enterprises have developed various types of web APIs to support their business services, such as Google Map APIs, Twitter APIs, and eBay APIs. Due to the huge number of web APIs available in public domain, unfortunately, choosing relevant and low-risk web APIs has become an important problem for developers. This research is aimed at enhancing the recom- mendation engine for web APIs from several aspects. First, a new scanning technique is developed to detect the usage of web APIs in source codes. Using our scanning technique, we scanned over 1.7 million Open Source projects to capture the API usage patterns. Second, we integrated three machine learning models to predict compliance risks from web APIs based on their terms of services or other legal documents. Third, utilizing the knowledge learned from scanning results and compliance risks, we built a new recommendation engine for web APIs. We conducted an experimental study to evaluate our Web API recommendation engine and demonstrate its effectiveness. Some other modules, such as finding similar web APIs and searching function-related web APIs, have also been discussed. / Graduate
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/10510 |
| Date | 11 January 2019 |
| Creators | Qiu, Feng |
| Contributors | Wu, Kui |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
| Rights | Available to the World Wide Web |
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