الفهرس 
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Abstract
Bug localization is considered one of the most difficult activities in the software debugging
process. It is also vital to guarantee software reliability. Moreover, manual debugging that is
used in detecting faults is becoming more expensive and time-consuming especially when
dealing with programs that contain a huge number of lines. Hence, there has been a great
demand for bug localization techniques that can pinpoint faults for the developers. Various
software fault localization techniques based on graph mining have been proposed in the
literature. These techniques rely on detecting frequent sub-graphs between failing and passing
traces. However, these approaches may not be applicable when the bug does not appear in a
discriminative pattern.
On the other hand, there are approaches that focus on selecting potentially faulty program
components (statements or predicates) and then ranking these components according to its
suspiciousness degree. One of the difficulties encountered by such approaches is to understand
the context of fault occurrence, because it considers statements and predicates in isolation.
To address these issues, this dissertation introduces an approach that helps in analyzing
the context of execution traces based on control flow graphs. The proposed approach is based
on ranking edges connecting nodes representing basic blocks in software programs. Ranking
is performed using Dstar method that proved to be more effective than many fault
localization techniques. The proposed method helps in detecting some types of faults that
could not be previously detected by many other approaches. Experiments show the
effectiveness of the proposed approach compared to some well-known approaches such as
Dstar, Tarantula, SOBER, Cause Transition and Liblit05. For instance, when the percentage of
examined code is 30%, the proposed technique can localize nearly 81% of the faulty
versions, which outperforms the other four techniques.