Think Locally, Act Globally Improving Defect and Effort Prediction Models
Authors -
Nicolas, Bettenburg;
Meiyappan, Nagappan and
Ahmed, E. Hassan
Venue -
In Proceedings of the 9th Working Conference on Mining Software Repositories (MSR 2012). Zurich, Switzerland. June 2-3, 2012
Related Tags -
Abstract -
Much research energy in software engineering is
focused on the creation of effort and defect prediction models.
Such models are important means for practitioners to judge
their current project situation, optimize the allocation of their
resources, and make informed future decisions. However, software
engineering data contains a large amount of variability.
Recent research demonstrates that such variability leads to
poor fits of machine learning models to the underlying data, and suggests splitting datasets into more fine-grained subsets
with similar properties. In this paper, we present a comparison
of three different approaches for creating statistical regression
models to model and predict software defects and development
effort. Global models are trained on the whole dataset. In
contrast, local models are trained on subsets of the dataset.
Last, we build a global model that takes into account local
characteristics of the data. We evaluate the performance of
these three approaches in a case study on two defect and
two effort datasets. We find that for both types of data, local
models show a significantly increased fit to the data compared
to global models. The substantial improvements in both relative
and absolute prediction errors demonstrate that this increased
goodness of fit is valuable in practice. Finally, our experiments
suggest that trends obtained from global models are too general
for practical recommendations. At the same time, local models
provide a multitude of trends which are only valid for specific
subsets of the data. Instead, we advocate the use of trends
obtained from global models that take into account local
characteristics, as they combine the best of both worlds.
Preprint -
PDF
BibTex -
@article{Bettenburg2012_2,
author = {Nicolas, Bettenburg and Meiyappan, Nagappan and Ahmed, E. Hassan},
keyword = {Defect Prediction},
title = {Think Locally, Act Globally Improving Defect and Effort Prediction Models},
type = {conference},
venue = {In Proceedings of the 9th Working Conference on Mining Software Repositories (MSR 2012). Zurich, Switzerland. June 2-3, 2012}
}