Bug or Not? Bug Report Classification Using N-Gram IDF
This addresses the time-consuming task of manual bug report classification for software engineers, but it is incremental as it builds on existing IDF methods.
The paper tackled the problem of misclassified bug reports by proposing a classification model using N-gram IDF to extract key terms as features, achieving superior performance over topic-based models in all evaluated cases.
Previous studies have found that a significant number of bug reports are misclassified between bugs and non-bugs, and that manually classifying bug reports is a time-consuming task. To address this problem, we propose a bug reports classification model with N-gram IDF, a theoretical extension of Inverse Document Frequency (IDF) for handling words and phrases of any length. N-gram IDF enables us to extract key terms of any length from texts, these key terms can be used as the features to classify bug reports. We build classification models with logistic regression and random forest using features from N-gram IDF and topic modeling, which is widely used in various software engineering tasks. With a publicly available dataset, our results show that our N-gram IDF-based models have a superior performance than the topic-based models on all of the evaluated cases. Our models show promising results and have a potential to be extended to other software engineering tasks.