@inproceedings{paper:panjer:2007,
title = {Predicting Eclipse Bug Lifetimes},
author = {Lucas D. Panjer},
booktitle = {Proceedings of the Fourth International Workshop on Mining Software Repositories},
month = {May},
pages = {29},
publisher = {IEEE Computer Society},
url = {http://portal.acm.org/ft_gateway.cfm?id=1269043&type=pdf&coll=&dl=ACM&CFID=67864570&CFTOKEN=63039214},
year = {2007},
description = {predicting lifetimes of bugs with the use of machine learning approaches (0-R, 1-R, Decision Trees, Naive Bayes, Logistic Regression)},
abstract = {In non-trivial software development projects planning
and allocation of resources is an important and difficult
task. Estimation of work time to fix a bug is commonly used
to support this process. This research explores the viability
of using data mining tools to predict the time to fix a bug
given only the basic information known at the beginning of
a bug’s lifetime. To address this question, a historical portion
of the Eclipse Bugzilla database is used for modeling
and predicting bug lifetimes. A bug history transformation
process is described and several data mining models are
built and tested. Interesting behaviours derived from the
models are documented. The models can correctly predict
up to 34.9% of the bugs into a discretized log scaled lifetime
class.},
keywords = {bug eclipse lifetime }
}
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