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.
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