View article

Correctly localizing buggy files for bug reports together with their semantic and structural information is a crucial task, which would essentially improve the accuracy of bug localization techniques. To empirically evaluate and demonstrate the effects of both semantic and structural information in bug reports and source files on improving the performance of bug localization, we propose CNN_Forest involving convolutional neural network and ensemble of random forests that have excellent performance in the tasks of semantic parsing and structural information extraction. We first employ convolutional neural network with multiple filters and an ensemble of random forests with multi-grained scanning to extract semantic and structural features from the word vectors derived from bug reports and source files. And a subsequent cascade forest (a cascade of ensembles of random forests) is used to …