A framework for the discovery of predictive fix-time models (Contributo in atti di convegno)

Type
Label
  • A framework for the discovery of predictive fix-time models (Contributo in atti di convegno) (literal)
Anno
  • 2014-01-01T00:00:00+01:00 (literal)
Alternative label
  • Folino, Francesco; Guarascio, Massimo; Pontieri, Luigi (2014)
    A framework for the discovery of predictive fix-time models
    in 16th International Conference on Enterprise Information Systems (ICEIS 2014), Lisbon; Portugal, 27-30 April 2014
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Folino, Francesco; Guarascio, Massimo; Pontieri, Luigi (literal)
Pagina inizio
  • 99 (literal)
Pagina fine
  • 108 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://www.scopus.com/record/display.url?eid=2-s2.0-84902352063&origin=inward (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
  • Proceedings of the 16th International Conference on Enterprise Information Systems (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 1 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana
  • 1 (literal)
Note
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Istituto Di Calcolo E Reti Ad Alte Prestazioni, Rende (literal)
Titolo
  • A framework for the discovery of predictive fix-time models (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 9789897580277 (literal)
Abstract
  • Fix-time prediction is a key task in bug tracking systems, which has been recently faced through the definition of inductive learning methods, trained to estimate the time needed to solve a case at the moment when it is reported. And yet, the actions performed on a bug along its life can help refine the prediction of its (remaining) fix time, possibly with the help of Process Mining techniques. However, typical bug-tracking systems lack any task-oriented description of the resolution process, and store fine-grain records, just capturing bug attributes' updates. Moreover, no general approach has been proposed to support the definition of derived data, which can help improve considerably fix-time predictions. A new methodological framework for the analysis of bug repositories is presented here, along with an associated toolkit, leveraging two kinds of tools: (i) a combination of modular and flexible data-transformation mechanisms, for producing an enhanced process-oriented view of log data, and (ii) a series of ad-hoc induction techniques, for extracting a prediction model out of such a view. Preliminary results on the bug repository of a real project confirm the validity of our proposal and, in particular, of our log transformation methods. (literal)
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