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<title>Year-2011</title>
<link>http://repository.iiitd.edu.in/xmlui/handle/123456789/16</link>
<description/>
<pubDate>Sat, 11 Apr 2026 12:42:49 GMT</pubDate>
<dc:date>2026-04-11T12:42:49Z</dc:date>
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<title>Static technique for bug localization using character N-Gram based information retrieval model</title>
<link>http://repository.iiitd.edu.in/xmlui/handle/123456789/17</link>
<description>Static technique for bug localization using character N-Gram based information retrieval model
Sangeeta
Bug or Fault localization is a process of identifying the speci c location(s) or region(s) of&#13;
source code (at various granularity levels such as the directory path,  le, method or state-&#13;
ment) that is faulty and needs to be modi ed to repair the defect. Bug localization is a&#13;
routine task in software maintenance (corrective maintenance). Due to the increasing size&#13;
and complexity of current software applications, automated solutions for bug localization&#13;
can signi cantly reduce human e ort and software maintenance cost.&#13;
We presented a technique (which falls into the class of static techniques for bug localiza-&#13;
tion) for bug localization using a character N-gram based Information Retrieval (IR) model.&#13;
We framed the problem of bug localization as a relevant document(s) search task for a given&#13;
query and investigated the application of character-level N-gram based textual features de-&#13;
rived from bug reports and source-code  le attributes. We implemented the proposed IR&#13;
model and evaluated its performance on dataset downloaded from two popular open-source&#13;
projects (JBOSS and Apache).&#13;
We conducted a series of experiments to validate our hypothesis and presented evidences&#13;
to demonstrate that the proposed approach is e ective. The accuracy of the proposed ap-&#13;
proach is measured in terms of the standard and commonly used SCORE and MAP (Mean&#13;
Average Precision) metrics for the task of bug localization. Experimental results reveal that&#13;
the median value for the SCORE metric for JBOSS and Apache dataset is 99.03% and 93.70%&#13;
respectively. We observed that for 16.16% of the bug reports in the JBOSS dataset and for&#13;
10.67% of the bug reports in the Apache dataset, the average precision value (computed at&#13;
all recall levels) is between 0.9 and 1.0.
</description>
<pubDate>Wed, 14 Mar 2012 10:36:50 GMT</pubDate>
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<dc:date>2012-03-14T10:36:50Z</dc:date>
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