  The software inevitably encounters the crash, which will take developers a
large amount of effort to find the fault causing the crash (short for crashing
fault). Developing automatic methods to identify the residence of the crashing
fault is a crucial activity for software quality assurance. Researchers have
proposed methods to predict whether the crashing fault resides in the stack
trace based on the features collected from the stack trace and faulty code,
aiming at saving the debugging effort for developers. However, previous work
usually neglected the feature preprocessing operation towards the crash data
and only used traditional classification models. In this paper, we propose a
novel crashing fault residence prediction framework, called ConDF, which
consists of a consistency based feature subset selection method and a
state-of-the-art deep forest model. More specifically, first, the feature
selection method is used to obtain an optimal feature subset and reduce the
feature dimension by reserving the representative features. Then, a simplified
deep forest model is employed to build the classification model on the reduced
feature set. The experiments on seven open source software projects show that
our ConDF method performs significantly better than 17 baseline methods on
three performance indicators.
