A Fast Algorithm to Locate Concepts in Execution Traces. Medini, S., Galinier, P., Di Penta, M., Gu�h�neuc, Y., & Antoniol, G. In Cohen, M. & Cinn�ide, M. �, editors, Proceedings of the 3<sup>rd</sup> International Symposium on Search-based Software Engineering (SSBSE), pages 252–266, September, 2011. IEEE CS Press. 10 pages.
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Paper abstract bibtex
Paper abstract bibtex The identification of cohesive segments in execution traces is a important step in concept location which, in turns, is of paramount importance for many program-comprehension activities. In this paper, we reformulate the trace segmentation problem as a dynamic programming problem. Differently to approaches based on genetic algorithms, dynamic programming can compute an exact solution with better performance than previous approaches, even on long traces. We describe the new problem formulation and the algorithmic details of our approach. We then compare the performances of dynamic programming with those of a genetic algorithm, showing that dynamic programming reduces dramatically the time required to segment traces, without sacrificing precision and recall; even slightly improving them.
@INPROCEEDINGS{Medini11-SSBSE-FastConceptSplitting,
AUTHOR = {Soumaya Medini and Philippe Galinier and
Di Penta, Massimiliano and Yann-Ga�l Gu�h�neuc and Giuliano Antoniol},
BOOKTITLE = {Proceedings of the 3<sup>rd</sup> International Symposium on Search-based Software Engineering (SSBSE)},
TITLE = {A Fast Algorithm to Locate Concepts in Execution Traces},
YEAR = {2011},
OPTADDRESS = {},
OPTCROSSREF = {},
EDITOR = {Myra Cohen and Mel � Cinn�ide},
MONTH = {September},
NOTE = {10 pages.},
OPTNUMBER = {},
OPTORGANIZATION = {},
PAGES = {252--266},
PUBLISHER = {IEEE CS Press},
OPTSERIES = {},
OPTVOLUME = {},
KEYWORDS = {Topic: <b>Requirements and features</b>,
Venue: <c>SSBSE</c>},
URL = {http://www.ptidej.net/publications/documents/SSBSE11b.doc.pdf},
PDF = {http://www.ptidej.net/publications/documents/SSBSE11b.ppt.pdf},
ABSTRACT = {The identification of cohesive segments in execution
traces is a important step in concept location which, in turns, is of
paramount importance for many program-comprehension activities. In
this paper, we reformulate the trace segmentation problem as a
dynamic programming problem. Differently to approaches based on
genetic algorithms, dynamic programming can compute an exact solution
with better performance than previous approaches, even on long
traces. We describe the new problem formulation and the algorithmic
details of our approach. We then compare the performances of dynamic
programming with those of a genetic algorithm, showing that dynamic
programming reduces dramatically the time required to segment traces,
without sacrificing precision and recall; even slightly improving
them.}
}Downloads: 0
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