This study explores a novel approach to predicting key bug-related outcomes, including the time to resolution, time to fix, and ultimate status of a bug, using data from the Bugzilla Eclipse Project. Specifically, we leverage features available before a bug is resolved to enhance predictive accuracy. Our methodology incorporates sentiment analysis to derive both an emotionality score and a sentiment classification (positive or negative). Additionally, we integrate the bug's priority level and its topic, extracted using a BERTopic model, as features for a Convolutional Neural Network (CNN) and a Multilayer Perceptron (MLP). Our findings indicate that the combination of BERTopic and sentiment analysis can improve certain model performance metrics. Furthermore, we observe that balancing model inputs enhances practical applicability, albeit at the cost of a significant reduction in accuracy in most cases. To address our primary objectives, predicting time-to-resolution, time-to-fix, and bug destiny, we employ both binary classification and exact time value predictions, allowing for a comparative evaluation of their predictive effectiveness. Results demonstrate that sentiment analysis serves as a valuable predictor of a bug's eventual outcome, particularly in determining whether it will be fixed. However, its utility is less pronounced when classifying bugs into more complex or unconventional outcome categories.

本研究解决了大型开源软件库中缺陷相关结果预测的问题，包括解决时间、修复时间及缺陷最终状态。我们提出了一种新方法，结合情感分析及BERTopic模型，利用尚未解决的缺陷特征来增强预测的准确性。研究发现，情感分析在缺陷最终结果预测中具有重要价值，特别是在判断缺陷是否会被修复方面。

大型开源软件库中的缺陷命运预测：通过情感分析和BERT主题建模