Abstract In this paper, we present the results of a manual assessment on the coherence between the comment and the implementation of 3636 methods in three open source software applications (for one of these applications, we considered two different subsequent versions) implemented in Java. The results of this assessment have been collected in a dataset we made publicly available on the Web. The creation of this dataset is based on a protocol that is detailed in this paper. We present that protocol to let researchers evaluate the goodness of our dataset and to ease its future possible extensions. Another contribution of this paper consists in preliminarily investigating on the effectiveness of adopting a Vector Space Model (VSM) with the tf-idf schema to discriminate coherent and non-coherent 13 methods. We observed that the lexical similarity alone is not sufficient for this distinction, while encouraging results have been obtained by applying an Support Vector Machine (SVM) classifier on the whole vector space.
Coherence of comments and method implementations: a dataset and an empirical investigation
SCANNIELLO, GIUSEPPE
2018
Abstract
Abstract In this paper, we present the results of a manual assessment on the coherence between the comment and the implementation of 3636 methods in three open source software applications (for one of these applications, we considered two different subsequent versions) implemented in Java. The results of this assessment have been collected in a dataset we made publicly available on the Web. The creation of this dataset is based on a protocol that is detailed in this paper. We present that protocol to let researchers evaluate the goodness of our dataset and to ease its future possible extensions. Another contribution of this paper consists in preliminarily investigating on the effectiveness of adopting a Vector Space Model (VSM) with the tf-idf schema to discriminate coherent and non-coherent 13 methods. We observed that the lexical similarity alone is not sufficient for this distinction, while encouraging results have been obtained by applying an Support Vector Machine (SVM) classifier on the whole vector space.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.