Download Related Defect Report Detection using an Information Retrieval

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Number of (unique) users
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Number of requests
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CHAPTER 9. RELATED DEFECT FINDER (RDF)
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Time in days
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Time in days
(a) The number of defect search requests which were (b) The number of unique users that accessed the
executed per day.
RDF tool per day.
Figure 9.6: Usage of the RDF tool during the evaluation period of one month.
mentioned in these observations are different from the experts which were involved in the voting process (Section
5.3). These developers started using RDF after it was promoted on the Océ intranet.
Observation 1: State change
In this first observation we were able to link a search request in RDF, to a state change in Synergy.
In this first step Developer 9 was searching for defects related to ve070#20646 using RDF (Table 9.1). The
optimized algorithm was selected to execute the search query. After submitting the search request, the result
list in Figure 9.7 was shown to Developer 9.
Table 9.1: Database entry of the defect request table.
User name
Developer 9
Date time
2013-08-07 18:44:44
Defect id
ve070#20646
Algorithm
Optimized
In Table 9.2 we can observe that Developer 9 opened defect id ve070#20638, which showed up at the first
position, 15 seconds after submitting the search request.
Table 9.2: Database entry of the clicked results table.
User name
Developer 9
Requested
defect id
ve070#20646
Clicked
defect id
ve070#24638
Opening time
Closing time
2013-08-07 18:44:59
2013-08-07 18:46:50
Result
position
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Finally we take a look at the transition log of the particular defect. In Figure 9.8 a fragment of the transition
log is shown. In this transition log, the particular defect is marked as duplicate about 12 minutes after closing
defect ve070#20638 by the same person, Developer 9.
Defect ve070#20646 was entered more than a year ago. Using the RDF tool Developer 9 observed that the
defect was already fixed. The entire analysis took less than 14 minutes.
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