  The aim is to identify faulty predicates which have strong effect on program
failure. Statistical debugging techniques are amongst best methods for
pinpointing defects within the program source code. However, they have some
drawbacks. They require a large number of executions to identify faults, they
might be adversely affected by coincidental correctness, and they do not take
into consideration fault-proneness associated with different parts of the
program code while constructing behavioral models. Additionally, they do not
consider the simultaneous impact of predicates on program termination status.
To deal with mentioned problems, a new fault-proneness-aware approach based on
elastic net regression, namely FPA-Debug has been proposed in this paper.
FPA-Debug employs a clustering-based strategy to alleviate coincidental
correctness in fault localization and finds the smallest effective subset of
program predicates known as bug predictors. Moreover, the approach considers
fault-proneness of code during statistical modeling through applying different
regularization parameter to each program predicates depending on its location
within program source code. The experimental results on well-known test suite,
Siemens, reveal the effectiveness and accuracy of the FPA-Debug.
