AURA: A Hybrid Approach to Identify Framework Evolution. Wu, W., Guéhéneuc, Y., Antoniol, G., & Kim, M. In Devanbu, P. & Uchitel, S., editors, Proceedings of the 32<sup>nd</sup> International Conference on Software Engineering (ICSE), pages 325–334, May, 2010. ACM Press. 10 pages.Paper abstract bibtex Software frameworks and libraries are indispensable to today's software systems. As they evolve, it is often time-consuming for developers to keep their code up-to-date, so approaches have been proposed to facilitate this. Usually, these approaches cannot automatically identify change rules for one-replaced-by-many and many-replaced-by-one methods, and they trade off recall for higher precision using one or more experimentally-evaluated thresholds. We introduce AURA, a novel hybrid approach that combines call dependency and text similarity analyses to overcome these limitations. We implement it in a Java system and compare it on five frameworks with three previous approaches by Dagenais and Robillard, M.\ Kim \ygg@latinet al., and Schäfer \ygg@latinet al. The comparison shows that, on average, the recall of AURA is 53.07% higher while its precision is similar, \ygg@latine.g., 0.10% lower.
@INPROCEEDINGS{Wu10-ICSE-AURA,
author = {Wei Wu and Yann-Ga{\"e}l Gu{\'e}h{\'e}neuc and Giuliano Antoniol and Miryung Kim},
title = {{AURA}: A Hybrid Approach to Identify Framework Evolution},
booktitle = {Proceedings of the 32<sup>{nd}</sup> International Conference on Software Engineering (ICSE)},
year = {2010},
month = {May},
editor = {Prem Devanbu and Sebastian Uchitel},
publisher = {ACM Press},
note = {10 pages.},
abstract = {Software frameworks and libraries are indispensable to today's software systems. As they evolve, it is often time-consuming for developers to keep their code up-to-date, so approaches have been proposed to facilitate this. Usually, these approaches cannot automatically identify change rules for one-replaced-by-many and many-replaced-by-one methods, and they trade off recall for higher precision using one or more experimentally-evaluated thresholds. We introduce AURA, a novel hybrid approach that combines call dependency and text similarity analyses to overcome these limitations. We implement it in a Java system and compare it on five frameworks with three previous approaches by Dagenais and Robillard, M.\ Kim \ygg@latin{et al.}, and Sch\"afer \ygg@latin{et al.} The comparison shows that, on average, the recall of AURA is 53.07\% higher while its precision is similar, \ygg@latin{e.g.}, 0.10\% lower.},
grant = {NSERC DG and CRC on Software Patterns},
keywords = {Evolution patterns ; ICSE},
kind = {MISA},
language = {english},
url = {http://www.ptidej.net/publications/documents/ICSE10b.doc.pdf},
pdf = {http://www.ptidej.net/publications/documents/ICSE10b.ppt.pdf},
pages = {325--334}
}
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