Abstract
Bug reports typically contain detailed descriptions of failures, hints at the location of the corresponding defects, and discussions. Developers usually resolve bugs using comments in descriptions and discussions. The time to fix a bug varies greatly. Previous studies have investigated bug reports, but the influence of comments on bug fixing time is not well understood. This study adopts a convolutional neural network (CNN) and gradient-based visualization approach called Grad-cam to elucidate the impact of comments on bug fixing time and extract features. A feature represents an observed characteristic in a bug report when processing via deep learning. Specifically, CNN classifies bug reports, and then Grad-cam visualizes the decision basis of CNN by identifying the top 10 word sequences used in the prediction. Here, the features are major word sequences extracted by Grad-cam. In an experiment, the proposed method classified more than 36,000 actual bug reports from Bugzilla with an accuracy of 75%–80%. Additionally, the visualization highlighted differences in the stack trace and word abstraction by bug fixing time. Bug reports with short bug fixing times are concrete, whereas those with a long bug fixing time are abstract.
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