Bug Severity Prediction Algorithm Using Topic-Based Feature Selection and CNN-LSTM Algorithm

Abstract

Increasing software usage has gradually increased the occurrence of bugs. When writing a bug report, the severity of the bug can be freely selected, so the subjective judgment of the author is involved. In subjective judgment, a severity error may occur depending on the background knowledge between the user and the developer. To resolve this problem, in this paper, the severity was predicted using the feature selection algorithm of the severity of each topic. We utilize the dataset in Eclipse and Mozilla open source projects. First, we classify bug reports by topic-based severity, and extract features from the severity of each topic. The severity was predicted by learning the characteristics from the CNN-LSTM algorithm, and the F-measure was 90.62% and 93.22% of Mozilla. To evaluate the effectiveness of the proposed model, we compared the baselines including DeepSeverity and EWD-Multinomial studies with Eclipse and Mozilla open source projects and showed that the proposed model is more efficient.

Keywords

• CNN-LSTM
• feature selection
• topic modeling
• bug severity prediction
• software evolution