C. Weiss, R. Premraj, T. Zimmermann, and A. Zeller. Proceedings of the Fourth International Workshop on Mining Software Repositories, (May 2007)predicting time and effort of fixing a bug based on evaluations with the eclipse bugzilla dataset.
Abstract
Predicting the time and effort for a software problem has
long been a difficult task. We present an approach that automatically
predicts the fixing effort, i.e., the person-hours
spent on fixing an issue. Our technique leverages existing
issue tracking systems: given a new issue report, we use
the Lucene framework to search for similar, earlier reports
and use their average time as a prediction. Our approach
thus allows for early effort estimation, helping in assigning
issues and scheduling stable releases. We evaluated our
approach using effort data from the JBoss project. Given
a sufficient number of issues reports, our automatic predictions
are close to the actual effort; for issues that are bugs,
we are off by only one hour, beating na¨ıve predictions by a
factor of four.
%0 Conference Paper
%1 paper:weis:2007
%A Weiss, Cathrin
%A Premraj, Rahul
%A Zimmermann, Thomas
%A Zeller, Andreas
%B Proceedings of the Fourth International Workshop on Mining Software Repositories
%D 2007
%E Gall, Harald
%E Lanza, Michele
%K 2007 bug eclipse fix
%T How Long will it Take to Fix This Bug?
%U http://www.st.cs.uni-sb.de/publications/files/weiss-msr-2007.pdf
%X Predicting the time and effort for a software problem has
long been a difficult task. We present an approach that automatically
predicts the fixing effort, i.e., the person-hours
spent on fixing an issue. Our technique leverages existing
issue tracking systems: given a new issue report, we use
the Lucene framework to search for similar, earlier reports
and use their average time as a prediction. Our approach
thus allows for early effort estimation, helping in assigning
issues and scheduling stable releases. We evaluated our
approach using effort data from the JBoss project. Given
a sufficient number of issues reports, our automatic predictions
are close to the actual effort; for issues that are bugs,
we are off by only one hour, beating na¨ıve predictions by a
factor of four.
@inproceedings{paper:weis:2007,
abstract = {Predicting the time and effort for a software problem has
long been a difficult task. We present an approach that automatically
predicts the fixing effort, i.e., the person-hours
spent on fixing an issue. Our technique leverages existing
issue tracking systems: given a new issue report, we use
the Lucene framework to search for similar, earlier reports
and use their average time as a prediction. Our approach
thus allows for early effort estimation, helping in assigning
issues and scheduling stable releases. We evaluated our
approach using effort data from the JBoss project. Given
a sufficient number of issues reports, our automatic predictions
are close to the actual effort; for issues that are bugs,
we are off by only one hour, beating na¨ıve predictions by a
factor of four.},
added-at = {2008-05-13T15:38:34.000+0200},
author = {Weiss, Cathrin and Premraj, Rahul and Zimmermann, Thomas and Zeller, Andreas},
biburl = {https://www.bibsonomy.org/bibtex/29bf587447d9e1a671be67c4cd5b2cc55/mschuber},
booktitle = {Proceedings of the Fourth International Workshop on Mining Software Repositories},
editor = {Gall, Harald and Lanza, Michele},
interhash = {39209ef42252fa5dc6080f3cd89e627b},
intrahash = {9bf587447d9e1a671be67c4cd5b2cc55},
keywords = {2007 bug eclipse fix},
month = May,
note = {predicting time and effort of fixing a bug based on evaluations with the eclipse bugzilla dataset},
timestamp = {2008-09-09T12:58:24.000+0200},
title = {How Long will it Take to Fix This Bug?},
url = {http://www.st.cs.uni-sb.de/publications/files/weiss-msr-2007.pdf},
year = 2007
}