Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering 2016
DOI: 10.1145/2970276.2975938
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Statistical analysis of large sets of models

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Cited by 9 publications
(7 citation statements)
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“…Note that the number of clone pairs for NICAD-Ecore are extracted from the clone clusters (i.e. taking each pair in all the clusters), and differ slightly from the original clone pairs reported by NICAD-Ecore, which are 591 for Type B, and 1054 for Type C (see supplemental material 13 for the clone pair report of NICAD-Ecore). This is due to the fact that NICAD uses connected component analysis for building the clone clusters from pairs.…”
Section: Resultsmentioning
confidence: 99%
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“…Note that the number of clone pairs for NICAD-Ecore are extracted from the clone clusters (i.e. taking each pair in all the clusters), and differ slightly from the original clone pairs reported by NICAD-Ecore, which are 591 for Type B, and 1054 for Type C (see supplemental material 13 for the clone pair report of NICAD-Ecore). This is due to the fact that NICAD uses connected component analysis for building the clone clusters from pairs.…”
Section: Resultsmentioning
confidence: 99%
“…We discuss here the underlying concepts of the SAMOS framework [13,27] inspired by information retrieval (IR) and machine learning (ML). IR deals with effectively indexing, analyzing, searching and comparing various forms of content including natural language text documents [28].…”
Section: Preliminaries: Information Retrieval Vector Space Model Clusteringmentioning
confidence: 99%
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“…More research is needed on (1) finding the right chain of NLP tools applicable for models (in contrast with source code and documentation) and (2) reporting accuracies and disagreements between various tools (along the lines of the recent report in [9] for repository mining). c) Data Mining: Following the perspective of approaching MDE artefacts as data, we need scalable techniques to extract relevant units of information from models (features in data mining (DM) jargon), and to discover patterns including domain clusters, outliers/noise and clones (see example applications in [7], [8], [10]). To be able to analyse, explore and eventually make sense of the large datasets in MDE (e.g.…”
Section: Treating Mde Artefacts As Datamentioning
confidence: 99%