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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| Vydáno v: | 2018 IEEE/ACM 26th International Conference on Program Comprehension (ICPC) s. 233 - 243 |
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| Hlavní autoři: | , , |
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| Jazyk: | angličtina |
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ACM
01.05.2018
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| ISSN: | 2643-7171 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Hartel, Johannes Lammel, Ralf Aksu, Hakan |
| Author_xml | – sequence: 1 givenname: Johannes surname: Hartel fullname: Hartel, Johannes organization: University of Koblenz-Landau – sequence: 2 givenname: Hakan surname: Aksu fullname: Aksu, Hakan organization: University of Koblenz-Landau – sequence: 3 givenname: Ralf surname: Lammel fullname: Lammel, Ralf organization: University of Koblenz-Landau |
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| PublicationTitle | 2018 IEEE/ACM 26th International Conference on Program Comprehension (ICPC) |
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| Snippet | APIs can be classified according to the programming domains (e.g., GUIs, databases, collections, or security) that they address. Such classification is vital... |
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| StartPage | 233 |
| SubjectTerms | APIs Clustering exploration Computational modeling Correlation GitHub Hierarchical clustering. Feature modeling Java Large scale integration Maven Central Repository Programming Security Software Software development management |
| Title | Classification of APIs by Hierarchical Clustering |
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