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Using Machine Learning and Information Retrieval Techniques to Improve Software Maintainability

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Trustworthy Eternal Systems via Evolving Software, Data and Knowledge (EternalS 2012)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 379))

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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.

The research described in this paper has been partially supported by the European Community’s Seventh Framework Programme (FP7/2007-2013) under the grants #247758: EternalS – Trustworthy Eternal Systems via Evolving Software, Data and Knowledge, and #288024: LiMoSINe – Linguistically Motivated Semantic aggregation engiNes.

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Author information

Authors and Affiliations

  1. University of Naples “Federico II”, Italy

    Anna Corazza, Sergio Di Martino & Valerio Maggio

  2. University of Trento, Italy

    Alessandro Moschitti & Andrea Passerini

  3. University of Basilicata, Italy

    Giuseppe Scanniello

  4. ISTI Institute - CNR, Italy

    Fabrizio Silvestri

Authors
  1. Anna Corazza
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  2. Sergio Di Martino
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  3. Valerio Maggio
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  4. Alessandro Moschitti
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  5. Andrea Passerini
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  6. Giuseppe Scanniello
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  7. Fabrizio Silvestri
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Editor information

Editors and Affiliations

  1. Department of Information Engineering and Computer Science, University of Trento, Via Sommarive 5, 38123, Povo, Trento, Italy

    Alessandro Moschitti  & Barbara Plank  & 

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Corazza, A. et al. (2013). Using Machine Learning and Information Retrieval Techniques to Improve Software Maintainability. In: Moschitti, A., Plank, B. (eds) Trustworthy Eternal Systems via Evolving Software, Data and Knowledge. EternalS 2012. Communications in Computer and Information Science, vol 379. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45260-4_9

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  • DOI: https://doi.org/10.1007/978-3-642-45260-4_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-45259-8

  • Online ISBN: 978-3-642-45260-4

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