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    <link>http://repository.iiitd.edu.in/xmlui/handle/123456789/16</link>
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    <pubDate>Sun, 21 Jun 2026 12:43:28 GMT</pubDate>
    <dc:date>2026-06-21T12:43:28Z</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>Title: Static technique for bug localization using character N-Gram based information retrieval model
Authors: Sangeeta
Abstract: Bug or Fault localization is a process of identifying the speci c location(s) or region(s) of&#xD;
source code (at various granularity levels such as the directory path,  le, method or state-&#xD;
ment) that is faulty and needs to be modi ed to repair the defect. Bug localization is a&#xD;
routine task in software maintenance (corrective maintenance). Due to the increasing size&#xD;
and complexity of current software applications, automated solutions for bug localization&#xD;
can signi cantly reduce human e ort and software maintenance cost.&#xD;
We presented a technique (which falls into the class of static techniques for bug localiza-&#xD;
tion) for bug localization using a character N-gram based Information Retrieval (IR) model.&#xD;
We framed the problem of bug localization as a relevant document(s) search task for a given&#xD;
query and investigated the application of character-level N-gram based textual features de-&#xD;
rived from bug reports and source-code  le attributes. We implemented the proposed IR&#xD;
model and evaluated its performance on dataset downloaded from two popular open-source&#xD;
projects (JBOSS and Apache).&#xD;
We conducted a series of experiments to validate our hypothesis and presented evidences&#xD;
to demonstrate that the proposed approach is e ective. The accuracy of the proposed ap-&#xD;
proach is measured in terms of the standard and commonly used SCORE and MAP (Mean&#xD;
Average Precision) metrics for the task of bug localization. Experimental results reveal that&#xD;
the median value for the SCORE metric for JBOSS and Apache dataset is 99.03% and 93.70%&#xD;
respectively. We observed that for 16.16% of the bug reports in the JBOSS dataset and for&#xD;
10.67% of the bug reports in the Apache dataset, the average precision value (computed at&#xD;
all recall levels) is between 0.9 and 1.0.</description>
      <pubDate>Wed, 14 Mar 2012 10:36:50 GMT</pubDate>
      <guid isPermaLink="false">http://repository.iiitd.edu.in/xmlui/handle/123456789/17</guid>
      <dc:date>2012-03-14T10:36:50Z</dc:date>
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