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TMAP: Discovering relevant API methods through text mining of API documentation.

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Bibliographic Details
Title: TMAP: Discovering relevant API methods through text mining of API documentation.
Authors: Pandita, Rahul, Jetley, Raoul, Sudarsan, Sithu, Menzies, Timothy, Williams, Laurie
Source: Journal of Software: Evolution & Process; Dec2017, Vol. 29 Issue 12, pn/a-N.PAG, 15p
Subject Terms: TEXT mining, APPLICATION program interfaces, COMPUTER software development, SOURCE code, JAVA programming language
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 and C# API; and (2) Java ME and Android 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. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
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 and C# API; and (2) Java ME and Android 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. [ABSTRACT FROM AUTHOR]
ISSN:20477473
DOI:10.1002/smr.1845