Noises in Interaction Traces Data and their Impact on Previous Research Studies. Soh, Z., Drioul, T., Rappe, P., Khomh, F., Gu�h�neuc, Y., & Habra, N. In Carver, J. & Dieste, O., editors, Proceedings of the 9<sup>th</sup> International Symposium of Empirical Software Engineering and Measurement (ESEM), pages 1–10, October, 2015. IEEE CS Press. 10 pages.
Paper abstract bibtex Context: Developers' interaction traces (ITs) are commonly used in software engineering to understand how developers maintain and evolve software systems. Researchers make several assumptions when mining ITs, e.g., edit events are considered to be change activities and the time mined from ITs is considered to be the time spent by the developers performing the maintenance task. Goal: We investigate the extent to which these assumptions are correct. We examine noises in developers'''' ITs data and the impact of these noises on previous results derived from these traces. Approach: We perform an experiment with 15 participants, whom we asked to perform bug-fixing activities and collect Mylyn ITs and VLC video captures. We then investigate noises between the two data sets and propose an approach to correct noises in ITs. Results: We find that Mylyn ITs can miss on average about 6\NOof the time spent performing a task and contain on average about 28\NOof false edit-events. We report that these noises may have led researchers to mislabel some participants'''' editing styles in about 34\NOof the cases and that the numbers of edit-events performed by developers and the times that they spent on tasks are correlated, when they were considered not to be. Conclusion: We show that ITs must be carefully cleaned before being used in research studies.
@INPROCEEDINGS{Soh15-ESEM-Noises,
AUTHOR = {Z�phyrin Soh and Thomas Drioul and Pierre-Antoine Rappe and
Foutse Khomh and Yann-Ga�l Gu�h�neuc and Naji Habra},
BOOKTITLE = {Proceedings of the 9<sup>th</sup> International Symposium of Empirical Software Engineering and Measurement (ESEM)},
TITLE = {Noises in Interaction Traces Data and their Impact on
Previous Research Studies},
YEAR = {2015},
OPTADDRESS = {},
OPTCROSSREF = {},
EDITOR = {Jeff Carver and Oscar Dieste},
MONTH = {October},
NOTE = {10 pages.},
OPTNUMBER = {},
OPTORGANIZATION = {},
PAGES = {1--10},
PUBLISHER = {IEEE CS Press},
OPTSERIES = {},
OPTVOLUME = {},
KEYWORDS = {Topic: <b>Program comprehension</b>, Venue: <c>ESEM</c>},
URL = {http://www.ptidej.net/publications/documents/ESEM15.doc.pdf},
PDF = {http://www.ptidej.net/publications/documents/ESEM15.ppt.pdf},
ABSTRACT = {Context: Developers' interaction traces (ITs) are
commonly used in software engineering to understand how developers
maintain and evolve software systems. Researchers make several
assumptions when mining ITs, e.g., edit events are considered to be
change activities and the time mined from ITs is considered to be the
time spent by the developers performing the maintenance task. Goal:
We investigate the extent to which these assumptions are correct. We
examine noises in developers'''' ITs data and the impact of these
noises on previous results derived from these traces. Approach: We
perform an experiment with 15 participants, whom we asked to perform
bug-fixing activities and collect Mylyn ITs and VLC video captures.
We then investigate noises between the two data sets and propose an
approach to correct noises in ITs. Results: We find that Mylyn ITs
can miss on average about 6\NOof the time spent performing a task and
contain on average about 28\NOof false edit-events. We report that
these noises may have led researchers to mislabel some
participants'''' editing styles in about 34\NOof the cases and that
the numbers of edit-events performed by developers and the times that
they spent on tasks are correlated, when they were considered not to
be. Conclusion: We show that ITs must be carefully cleaned before
being used in research studies.}
}
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Researchers make several assumptions when mining ITs, e.g., edit events are considered to be change activities and the time mined from ITs is considered to be the time spent by the developers performing the maintenance task. Goal: We investigate the extent to which these assumptions are correct. We examine noises in developers'''' ITs data and the impact of these noises on previous results derived from these traces. Approach: We perform an experiment with 15 participants, whom we asked to perform bug-fixing activities and collect Mylyn ITs and VLC video captures. We then investigate noises between the two data sets and propose an approach to correct noises in ITs. Results: We find that Mylyn ITs can miss on average about 6\\NOof the time spent performing a task and contain on average about 28\\NOof false edit-events. 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