Mining usage patterns for the Android API

Abstract

API methods are not used alone, but in groups and following patterns. However, despite being a key information for API users, most usage patterns are not described in official API documents. In this article, we report a study that evaluates the feasibility of automatically enriching API documents with information on usage patterns. For this purpose, we mine and analyze 1,952 usage patterns, from a set of 396 Android applications. As part of our findings, we report that the Android API has many undocumented and non-trivial usage patterns, which can be inferred using association rule mining algorithms. We also describe API methods are not used alone, but in groups and following some patterns. However, despite being a key information for API users, most usage patterns are not explicitly described in official API documents. In this article, we report a study to evaluate the feasibility of automatically enriching API documents with information on usage patterns. For this purpose, we extract and analyze 1,952 usage patterns, from a set of 396 Android applications. As part of our findings, we report that the Android API has many undocumented and non-trivial usage patterns, which can be inferred using association rule mining algorithms. We also describe a field study where a version of the original Android documentation was instrumented with the extracted usage patterns and source code examples. During 17 months, this documentation received 77,863 visits from real Android developers. .

A Survey of App Store Analysis for Software Engineering

“…Borges and Valente [30] used association rule mining to infer API usage patterns, using a dataset of 396 open source Android apps. For their study, the authors extended APIMiner [167] to mine usage patterns and instrument API documents with extracted usage patterns.…”

On the use of replacement messages in API deprecation: An empirical study

“…Hora et al [2012Hora et al [ , 2013 focus on the extraction of API evolution rules that only make sense for a system or domain under analysis. Using association rule mining algorithms, Borges and Valente [2015] mine 1,952 usage patterns, from a set of 396 Android applications. They report that the Android API has many undocumented and non-trivial usage patterns.…”

Summarizing Software API Usage Examples Using Clustering Techniques

“…APIMiner [20] introduces a summarization algorithm that uses slicing to preserve only the API-relevant statements of the source code. Further work by the same authors [4] incorporates association rule techniques, and employs an improved version of the summarization algorithm, with the aim of resolving variable types and adding descriptive comments. Yet the system does not cluster similar examples, while most examples show the usage of a single API method.…”