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Last updated on Thursday, November 16, 2023
@inproceedings{Porru2016PROMISE,
author = {Simone Porru and Alessandro Murgia and Serge Demeyer and
Michele Marchesi and Roberto Tonelli},
booktitle = {Proceedings {PROMISE 2016} (The 12th International
Conference on Predictive Models and Data Analytics in
Software Engineering)},
note = {Acceptance ratio: unknown},
publisher = {ACM},
title = {Estimating Story Points from Issue Reports},
year = {2016},
abstract = {Estimating the effort of software engineering tasks
is notoriously hard but essential for project
planning. The agile community often adopts issue
reports to describe tasks, and story points to
estimate task effort. In this paper, we propose a
machine learning classifier for estimating the story
points required to address an issue. Through
empirical evaluation on one industrial project and
eight open source projects, we demonstrate that such
classifier is feasible. We show that ---after an
initial training on over 300 issue reports--- the
classifier estimates a new issue in less than 15
seconds with a mean magnitude of relative error
between 0.16 and 0.61. In addition, issue type,
summary, description, and related components prove to
be project dependent features pivotal for story point
estimation.},
annote = {internationalconference},
doi = {10.1145/2972958.2972959},
}