Using Machine Learning and Information Retrieval Techniques to Improve Software Maintainability

Anna Corazza, Sergio Di Martino, Valerio Maggio, Alessandro Moschitti, Andrea Passerini, Giuseppe Scanniello, Fabrizio Silvestri

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationCommunications in Computer and Information Science
PublisherSpringer Verlag
Pages117-134
Number of pages18
Volume379 CCIS
ISBN (Print)9783642452598
DOIs
Publication statusPublished - 1 Jan 2013
Externally publishedYes
Event2nd International Workshop on Trustworthy Eternal Systems via Evolving Software, Data and Knowledge, EternalS 2012 - Montpellier, France
Duration: 28 Aug 201228 Aug 2012

Publication series

NameCommunications in Computer and Information Science
Volume379 CCIS
ISSN (Print)18650929

Other

Other2nd International Workshop on Trustworthy Eternal Systems via Evolving Software, Data and Knowledge, EternalS 2012
CountryFrance
CityMontpellier
Period28/8/1228/8/12

Fingerprint

Maintainability
Information retrieval
Learning systems
Recovery
Computer software maintenance
Software architecture
Processing

Keywords

  • Information Retrieval
  • Machine Learning
  • Natural Language Processing
  • Software Maintenance and Evolution

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Corazza, A., Di Martino, S., Maggio, V., Moschitti, A., Passerini, A., Scanniello, G., & Silvestri, F. (2013). Using Machine Learning and Information Retrieval Techniques to Improve Software Maintainability. In Communications in Computer and Information Science (Vol. 379 CCIS, pp. 117-134). (Communications in Computer and Information Science; Vol. 379 CCIS). Springer Verlag. https://doi.org/10.1007/978-3-642-45260-4_9

Using Machine Learning and Information Retrieval Techniques to Improve Software Maintainability. / Corazza, Anna; Di Martino, Sergio; Maggio, Valerio; Moschitti, Alessandro; Passerini, Andrea; Scanniello, Giuseppe; Silvestri, Fabrizio.

Communications in Computer and Information Science. Vol. 379 CCIS Springer Verlag, 2013. p. 117-134 (Communications in Computer and Information Science; Vol. 379 CCIS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Corazza, A, Di Martino, S, Maggio, V, Moschitti, A, Passerini, A, Scanniello, G & Silvestri, F 2013, Using Machine Learning and Information Retrieval Techniques to Improve Software Maintainability. in Communications in Computer and Information Science. vol. 379 CCIS, Communications in Computer and Information Science, vol. 379 CCIS, Springer Verlag, pp. 117-134, 2nd International Workshop on Trustworthy Eternal Systems via Evolving Software, Data and Knowledge, EternalS 2012, Montpellier, France, 28/8/12. https://doi.org/10.1007/978-3-642-45260-4_9
Corazza A, Di Martino S, Maggio V, Moschitti A, Passerini A, Scanniello G et al. Using Machine Learning and Information Retrieval Techniques to Improve Software Maintainability. In Communications in Computer and Information Science. Vol. 379 CCIS. Springer Verlag. 2013. p. 117-134. (Communications in Computer and Information Science). https://doi.org/10.1007/978-3-642-45260-4_9
Corazza, Anna ; Di Martino, Sergio ; Maggio, Valerio ; Moschitti, Alessandro ; Passerini, Andrea ; Scanniello, Giuseppe ; Silvestri, Fabrizio. / Using Machine Learning and Information Retrieval Techniques to Improve Software Maintainability. Communications in Computer and Information Science. Vol. 379 CCIS Springer Verlag, 2013. pp. 117-134 (Communications in Computer and Information Science).
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