fine-GRAPE: fine-grained APi usage extractor – an approach and dataset to investigate API usage

An Application Programming Interface (API) provides a set of functionalities to a developer with the aim of enabling reuse. APIs have been investigated from different angles such as popularity usage and evolution to get a better understanding of their various characteristics. For such studies, softw...

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Bibliographic Details
Published inEmpirical software engineering : an international journal Vol. 22; no. 3; pp. 1348 - 1371
Main Authors Sawant, Anand Ashok, Bacchelli, Alberto
Format Journal Article
LanguageEnglish
Published New York Springer US 01.06.2017
Springer Nature B.V
Subjects
Annotations
Application programming interface
Clients
Compilers
Computer Science
Data mining
Grapes
Interpreters
Lag time
Programming Languages
Repositories
Software Engineering/Programming and Operating Systems
API popularity
Application programming interface
API usage
Dataset
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Summary:An Application Programming Interface (API) provides a set of functionalities to a developer with the aim of enabling reuse. APIs have been investigated from different angles such as popularity usage and evolution to get a better understanding of their various characteristics. For such studies, software repositories are mined for API usage examples. However, many of the mining algorithms used for such purposes do not take type information into account. Thus making the results unreliable. In this paper, we aim to rectify this by introducing fine-GRAPE, an approach that produces fine-grained API usage information by taking advantage of type information while mining API method invocations and annotation. By means of fine-GRAPE, we investigate API usages from Java projects hosted on GitHub. We select five of the most popular APIs across GitHub Java projects and collect historical API usage information by mining both the release history of these APIs and the code history of every project that uses them. We perform two case studies on the resulting dataset. The first measures the lag time of each client. The second investigates the percentage of used API features. In the first case we find that for APIs that release more frequently clients are far less likely to upgrade to a more recent version of the API as opposed to clients of APIs that release infrequently. The second case study shows us that for most APIs there is a small number of features that is actually used and most of these features relate to those that have been introduced early in the APIs lifecycle.
ISSN:1382-3256
1573-7616
DOI:10.1007/s10664-016-9444-6