A Framework for the Discovery of Predictive Fix-time Models

Francesco Folino, Massimo Guarascio, Luigi Pontieri

2014

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.

Paper Citation

in Harvard Style

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 1: ICEIS, ISBN 978-989-758-027-7, pages 99-108. DOI: 10.5220/0004897400990108

in Bibtex Style

@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 1: ICEIS,},
year={2014},
pages={99-108},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004897400990108},
isbn={978-989-758-027-7},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - A Framework for the Discovery of Predictive Fix-time Models
SN - 978-989-758-027-7
AU - Folino F.
AU - Guarascio M.
AU - Pontieri L.
PY - 2014
SP - 99
EP - 108
DO - 10.5220/0004897400990108