Combining Probabilistic Ranking and Latent Semantic Indexing for Feature Identification. Poshyvanyk, D., Gu�h�neuc, Y., Marcus, A., Antoniol, G., & Rajlich, V. In Ebert, J. & Linos, P., editors, Proceedings of the 14<sup>th</sup> International Conference on Program Comprehension (ICPC), pages 137–148, June, 2006. IEEE CS Press. Best paper. 10 pages.
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Paper abstract bibtex
Paper abstract bibtex The paper recasts the problem of feature location in source code as a decision-making problem in the presence of uncertainty. The main contribution consists in the combination of two existing techniques for feature location in source code. Both techniques provide a set of ranked facts from the software, as result to the feature identification problem. One of the techniques is based on a Scenario Based Probabilistic ranking of events observed while executing a program under given scenarios. The other technique is defined as an information retrieval task, based on the Latent Semantic Indexing of the source code. We show the viability and effectiveness of the combined technique with two case studies. A first case study is a replication of feature identification in Mozilla, which allows us to directly compare the results with previously published data. The other case study is a bug location problem in Mozilla. The results show that the combined technique improves feature identification significantly with respect to each technique used independently.
@INPROCEEDINGS{Poshyvanyk06-ICPC-LSIFeature,
AUTHOR = {Denys Poshyvanyk and Yann-Ga�l Gu�h�neuc and
Andrian Marcus and Giuliano Antoniol and V�clav Rajlich},
BOOKTITLE = {Proceedings of the 14<sup>th</sup> International Conference on Program Comprehension (ICPC)},
TITLE = {Combining Probabilistic Ranking and Latent Semantic
Indexing for Feature Identification},
YEAR = {2006},
OPTADDRESS = {},
OPTCROSSREF = {},
EDITOR = {Jurgen Ebert and Panos Linos},
MONTH = {June},
NOTE = {Best paper. 10 pages.},
OPTNUMBER = {},
OPTORGANIZATION = {},
PAGES = {137--148},
PUBLISHER = {IEEE CS Press},
OPTSERIES = {},
OPTVOLUME = {},
KEYWORDS = {Topic: <b>Requirements and features</b>,
Venue: <c>ICPC</c>},
URL = {http://www.ptidej.net/publications/documents/ICPC06.doc.pdf},
PDF = {http://www.ptidej.net/publications/documents/ICPC06.ppt.pdf},
ABSTRACT = {The paper recasts the problem of feature location in
source code as a decision-making problem in the presence of
uncertainty. The main contribution consists in the combination of two
existing techniques for feature location in source code. Both
techniques provide a set of ranked facts from the software, as result
to the feature identification problem. One of the techniques is based
on a Scenario Based Probabilistic ranking of events observed while
executing a program under given scenarios. The other technique is
defined as an information retrieval task, based on the Latent
Semantic Indexing of the source code. We show the viability and
effectiveness of the combined technique with two case studies. A
first case study is a replication of feature identification in
Mozilla, which allows us to directly compare the results with
previously published data. The other case study is a bug location
problem in Mozilla. The results show that the combined technique
improves feature identification significantly with respect to each
technique used independently.}
}Downloads: 0
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