Improving Bug Location Using Binary Class Relationships. Ali, N., Saban�, A., Gu�h�neuc, Y., & Antoniol, G. In Ceccato, M. & Li, Z., editors, Proceedings of the 12<sup>th</sup> International Working Conference on Source Code Analysis and Manipulation (SCAM), pages 174–183, September, 2012. IEEE CS Press. 10 pages.
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Paper abstract bibtex
Paper abstract bibtex Bug location assists developers in locating culprit source code that must be modified to fix a bug. Done manually, it requires intensive search activities with unpredictable costs of effort and time. Information retrieval (IR) techniques have been proven useful to speedup bug location in object-oriented programs. IR techniques compute the textual similarities between a bug report and the source code to provide a list of potential culprit classes to developers. They rank the list of classes in descending order of the likelihood of the classes to be related to the bug report. However, due to the low textual similarity between source code and bug reports, IR techniques may put a culprit class at the end of a ranked list, which forces developers to manually verify all non-culprit classes before finding the actual culprit class. Thus, even with IR techniques, developers are not saved from manual effort. In this paper, we conjecture that binary class relationships (BCRs) could improve the rankings by IR techniques of classes and, thus, help reducing developers' manual effort. We present an approach, LIBCROOS, that combines the results of any IR technique with BCRs gathered through source code analyses. We perform an empirical study on four systems—Jabref, Lucene, muCommander, and Rhino—to compare the accuracy, in terms of rankings, of LIBCROOS with two IR techniques: latent semantic indexing (LSI) and vector space model (VSM). The results of this empirical study show that LIBCROOS improves the rankings of both IR technique statistically when compared to LSI and VSM alone and, thus, may reduce the developers' effort.
@INPROCEEDINGS{Ali12-SCAM-ImprovingBugLocationBCR,
AUTHOR = {Nasir Ali and Aminata Saban� and Yann-Ga�l Gu�h�neuc and
Giuliano Antoniol},
BOOKTITLE = {Proceedings of the 12<sup>th</sup> International Working Conference on Source Code Analysis and Manipulation (SCAM)},
TITLE = {Improving Bug Location Using Binary Class Relationships},
YEAR = {2012},
OPTADDRESS = {},
OPTCROSSREF = {},
EDITOR = {Mariano Ceccato and Zheng Li},
MONTH = {September},
NOTE = {10 pages.},
OPTNUMBER = {},
OPTORGANIZATION = {},
PAGES = {174--183},
PUBLISHER = {IEEE CS Press},
OPTSERIES = {},
OPTVOLUME = {},
KEYWORDS = {Topic: <b>Binary class relations</b>,
Topic: <b>Requirements and features</b>, Venue: <c>SCAM</c>},
URL = {http://www.ptidej.net/publications/documents/SCAM12.doc.pdf},
PDF = {http://www.ptidej.net/publications/documents/SCAM12.ppt.pdf},
ABSTRACT = {Bug location assists developers in locating culprit
source code that must be modified to fix a bug. Done manually, it
requires intensive search activities with unpredictable costs of
effort and time. Information retrieval (IR) techniques have been
proven useful to speedup bug location in object-oriented programs. IR
techniques compute the textual similarities between a bug report and
the source code to provide a list of potential culprit classes to
developers. They rank the list of classes in descending order of the
likelihood of the classes to be related to the bug report. However,
due to the low textual similarity between source code and bug
reports, IR techniques may put a culprit class at the end of a ranked
list, which forces developers to manually verify all non-culprit
classes before finding the actual culprit class. Thus, even with IR
techniques, developers are not saved from manual effort. In this
paper, we conjecture that binary class relationships (BCRs) could
improve the rankings by IR techniques of classes and, thus, help
reducing developers' manual effort. We present an approach, LIBCROOS,
that combines the results of any IR technique with BCRs gathered
through source code analyses. We perform an empirical study on four
systems---Jabref, Lucene, muCommander, and Rhino---to compare the
accuracy, in terms of rankings, of LIBCROOS with two IR techniques:
latent semantic indexing (LSI) and vector space model (VSM). The
results of this empirical study show that LIBCROOS improves the
rankings of both IR technique statistically when compared to LSI and
VSM alone and, thus, may reduce the developers' effort.}
}Downloads: 0
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Done manually, it requires intensive search activities with unpredictable costs of effort and time. Information retrieval (IR) techniques have been proven useful to speedup bug location in object-oriented programs. IR techniques compute the textual similarities between a bug report and the source code to provide a list of potential culprit classes to developers. They rank the list of classes in descending order of the likelihood of the classes to be related to the bug report. However, due to the low textual similarity between source code and bug reports, IR techniques may put a culprit class at the end of a ranked list, which forces developers to manually verify all non-culprit classes before finding the actual culprit class. Thus, even with IR techniques, developers are not saved from manual effort. In this paper, we conjecture that binary class relationships (BCRs) could improve the rankings by IR techniques of classes and, thus, help reducing developers' manual effort. We present an approach, LIBCROOS, that combines the results of any IR technique with BCRs gathered through source code analyses. We perform an empirical study on four systems—Jabref, Lucene, muCommander, and Rhino—to compare the accuracy, in terms of rankings, of LIBCROOS with two IR techniques: latent semantic indexing (LSI) and vector space model (VSM). 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Done manually, it \r\n requires intensive search activities with unpredictable costs of \r\n effort and time. Information retrieval (IR) techniques have been \r\n proven useful to speedup bug location in object-oriented programs. IR \r\n techniques compute the textual similarities between a bug report and \r\n the source code to provide a list of potential culprit classes to \r\n developers. They rank the list of classes in descending order of the \r\n likelihood of the classes to be related to the bug report. However, \r\n due to the low textual similarity between source code and bug \r\n reports, IR techniques may put a culprit class at the end of a ranked \r\n list, which forces developers to manually verify all non-culprit \r\n classes before finding the actual culprit class. Thus, even with IR \r\n techniques, developers are not saved from manual effort. In this \r\n paper, we conjecture that binary class relationships (BCRs) could \r\n improve the rankings by IR techniques of classes and, thus, help \r\n reducing developers' manual effort. We present an approach, LIBCROOS, \r\n that combines the results of any IR technique with BCRs gathered \r\n through source code analyses. We perform an empirical study on four \r\n systems---Jabref, Lucene, muCommander, and Rhino---to compare the \r\n accuracy, in terms of rankings, of LIBCROOS with two IR techniques: \r\n latent semantic indexing (LSI) and vector space model (VSM). 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