In this paper, we investigate some ideas based on Machine Learning, Natural Language Processing, and Information Retrieval to outline possible research directions in the field of software architecture recovery and clone detection. In particular, after presenting an extensive related work, we illustrate two proposals for addressing these two issues, that represent hot topics in the field of Software Maintenance. Both proposals use Kernel Methods for exploiting structural representation of source code and to automate the detection of clones and the recovery of the actually implemented architecture in a subject software system. © Springer-Verlag Berlin Heidelberg 2013.
Using Machine Learning and Information Retrieval Techniques to Improve Software Maintainability / Corazza, A.; Di Martino, S.; Maggio, V.; Moschitti, A.; Passerini, A.; Scanniello, G.; Silvestri, F.. - 379:(2013), pp. 117-134. (Intervento presentato al convegno 2nd International Workshop on Trustworthy Eternal Systems via Evolving Software, Data and Knowledge, EternalS 2012 tenutosi a Montpellier, fra) [10.1007/978-3-642-45260-4_9].
Using Machine Learning and Information Retrieval Techniques to Improve Software Maintainability
Silvestri F.
2013
Abstract
In this paper, we investigate some ideas based on Machine Learning, Natural Language Processing, and Information Retrieval to outline possible research directions in the field of software architecture recovery and clone detection. In particular, after presenting an extensive related work, we illustrate two proposals for addressing these two issues, that represent hot topics in the field of Software Maintenance. Both proposals use Kernel Methods for exploiting structural representation of source code and to automate the detection of clones and the recovery of the actually implemented architecture in a subject software system. © Springer-Verlag Berlin Heidelberg 2013.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
