TMAP: Discovering relevant API methods through text mining of API documentation. Issue 12 (20th February 2017)

• Record Type: Journal Article
• Authors: Pandita, Rahul; Jetley, Raoul; Sudarsan, Sithu; Menzies, Timothy; Williams, Laurie
• Other Names: Khomh Foutse guestEditor.; Lo David guestEditor.; Godfrey Michael W. guestEditor.
• Is Part Of: Journal of software. Volume 29:Issue 12(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-02-20
• Subjects: API documents -- API mappings -- text mining; Software engineering -- Periodicals; Computer software -- Development -- Periodicals; Software maintenance -- Periodicals; 005.1
• DOI: 10.1002/smr.1845
• Languages: English
• ISSNs: 2047-7473

Abstract

Abstract: Developers often migrate their applications to support various platform/programming‐language application programming interfaces (APIs) to retain existing users and to attract new users. To migrate an application written using 1 API (source) to another API (target), a developer must know how the methods in the source API map to the methods in the target API. Given that a typical platform or language exposes a large number of API methods, manually discovering API mappings is prohibitively resource‐intensive and may be error prone. The goal of this research is to support software developers in migrating an application from a source API to a target API by automatically discovering relevant method mappings across APIs using text mining on the natural language API method descriptions. This paper proposes text mining based approach (TMAP) to discover relevant API mappings. To evaluate our approach, we used TMAP to discover API mappings for 15 classes across (1)Java andC# API; and (2)Java ME andAndroid API. We compared the discovered mappings with state‐of‐the‐art source code analysis‐based approaches: Rosetta and StaMiner. Our results indicate that TMAP on average found relevant mappings for 56% and 57% more methods compared to the Rosetta and the StaMiner approaches, respectively.