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An information theoretic evaluation of software metrics for object lifetime prediction
Jeremy Singer, Sebastien Marion, Gavin Brown, Richard Jones, Mikel Lujan, Chris Ryder, and Ian Watson
In 2nd Workshop on Statistical and Machine learning approaches to ARchitectures and compilaTion (SMART'08), page 15, Goteborg, Sweden, January 2008.Abstract
Accurate object lifetime prediction can be exploited by allocators to improve the performance of generational garbage collection by placing immortal or long-lived objects directly into immortal or old generations. Object-oriented software metrics are emerging as viable indicators for object lifetime prediction. This paper studies the correlation of various metrics with object lifetimes. However, to date most studies have been empirical and have not provided any information theoretic underpinning. We use the information theoretic calculation of normalized mutual information to measure correlation. We assess which metrics are most useful for prediction and construct some simple yet accurate object lifetime predictors.
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Bibtex Record
@inproceedings{2641,
author = {Jeremy Singer and Sebastien Marion and Gavin Brown and Richard Jones and Mikel Lujan and Chris Ryder and Ian Watson},
title = {An Information Theoretic Evaluation of Software Metrics for Object Lifetime Prediction},
month = {January},
year = {2008},
pages = {15},
keywords = {Software metrics, Garbage collection, Object lifetime, Object-oriented programming, Java},
note = {},
doi = {},
url = {http://www.cs.kent.ac.uk/pubs/2008/2641},
publication_type = {inproceedings},
submission_id = {15434_1200933774},
booktitle = {2nd Workshop on Statistical and Machine learning approaches to ARchitectures and compilaTion (SMART'08)},
address = {Goteborg, Sweden},
refereed = {yes},
}