الفهرس 
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Abstract
Defective software modules are causing software failures, increasing development
and maintenance costs, and decreasing customer satisfaction. However, understanding
the impact of defects in various business applications is an essential way to improve
software quality. In software companies, bugs repositories become precious sources
for solving issues in different projects.
There are lots of factors related to bugs fixing that include identifying the defect
severity and its category. Severity which is defined as the impact of the bug in the
software system. Identifying bug severity is an important task commonly used to avoid
misclassification of the bug. While specifying bugs category before debugging will
lead to better handling of selecting the proper destination to fix it. For instance, some
reporters usually raise issues about the severity as critical for rapid repairing which is
not a correct behavior. This is from one side, from the other; the study would expect that
functionality bugs are fixed faster than other types of bugs due to their critical nature.
However, prior researches have often treated all bugs as similar which is not a good
way.
The presented thesis studies the feasibility of using efficient data mining techniques
to establish a model to predict the severity and category of a bug given the summary
of the bug only. Specifically, this can be done by using open source repositories of
the Eclipse and GNOM repositories for modeling and predicting bug severities. The
study compares the proposed algorithm and some other bench-marked algorithms. By
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