  Despite the proven applicability of the statistical methods in automatic
fault localization, these approaches are biased by data collected from
different executions of the program. This biasness could result in unstable
statistical models which may vary dependent on test data provided for trial
executions of the program. To resolve the difficulty, in this article a new
fault-proneness-aware statistical approach based on Elastic-Net regression,
namely FPA-FL is proposed. The main idea behind FPA-FL is to consider the
static structure and the fault-proneness of the program statements in addition
to their dynamic correlations with the program termination state. The grouping
effect of FPA-FL is helpful for finding multiple faults and supporting
scalability. To provide the context of failure, cause-effect chains of program
faults are discovered. FPA-FL is evaluated from different viewpoints on
well-known test suites. The results reveal high fault localization performance
of our approach, compared with similar techniques in the literature.
