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Authors: Francesco Folino ; Massimo Guarascio and Luigi Pontieri

Affiliation: Institute ICAR, Italy

Keyword(s): Data Mining, Business Process Analysis, Prediction, Bug Tracking.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Sensor Networks ; Signal Processing ; Soft Computing

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. (More)

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Paper citation in several formats:
Folino, F.; Guarascio, M. and Pontieri, L. (2014). A Framework for the Discovery of Predictive Fix-time Models. In Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 3: ICEIS; ISBN 978-989-758-027-7; ISSN 2184-4992, SciTePress, pages 99-108. DOI: 10.5220/0004897400990108

@conference{iceis14,
author={Francesco Folino. and Massimo Guarascio. and Luigi Pontieri.},
title={A Framework for the Discovery of Predictive Fix-time Models},
booktitle={Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 3: ICEIS},
year={2014},
pages={99-108},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004897400990108},
isbn={978-989-758-027-7},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 3: ICEIS
TI - A Framework for the Discovery of Predictive Fix-time Models
SN - 978-989-758-027-7
IS - 2184-4992
AU - Folino, F.
AU - Guarascio, M.
AU - Pontieri, L.
PY - 2014
SP - 99
EP - 108
DO - 10.5220/0004897400990108
PB - SciTePress