Classification of APIs by Hierarchical Clustering

APIs can be classified according to the programming domains (e.g., GUIs, databases, collections, or security) that they address. Such classification is vital in searching repositories (e.g., the Maven Central Repository for Java) and for understanding the technology stack used in software projects....

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Bibliographic Details
Published in2018 IEEE/ACM 26th International Conference on Program Comprehension (ICPC) pp. 233 - 243
Main Authors Hartel, Johannes, Aksu, Hakan, Lammel, Ralf
Format Conference Proceeding
LanguageEnglish
Published ACM 01.05.2018
Subjects
APIs
Clustering exploration
Computational modeling
Correlation
GitHub
Hierarchical clustering. Feature modeling
Java
Large scale integration
Maven Central Repository
Programming
Security
Software
Software development management
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Summary:APIs can be classified according to the programming domains (e.g., GUIs, databases, collections, or security) that they address. Such classification is vital in searching repositories (e.g., the Maven Central Repository for Java) and for understanding the technology stack used in software projects. We apply hierarchical clustering to a curated suite of Java APIs to compare the computed API clusters with preexisting API classifications. Clustering entails various parameters (e.g., the choice of IDF versus LSI versus LDA). We describe the corresponding variability in terms of a feature model. We exercise all possible con gurations to determine the maximum correlation with respect to two baselines: i) a smaller suite of APIs manually classified in previous research; ii) a larger suite of APIs from the Maven Central Repository, thereby taking advantage of crowd-sourced classification while relying on a threshold-based approach for identifying important APIs and versions thereof, subject to an API dependency analysis on GitHub. We discuss the configurations found in this way and we examine the influence of particular features on the correlation between computed clusters and baselines. To this end, we also leverage interactive exploration of the parameter space and the resulting dendrograms. In this manner, we can also identify issues with the use of classifiers (e.g., missing classifiers) in the baselines and limitations of the clustering approach.
ISSN:2643-7171
DOI:10.1145/3196321.3196344