Abstract
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.
Abstract (translated)
这项研究探索了一种新颖的方法，用于预测与软件缺陷相关的关键结果，包括解决时间、修复时间和最终状态。该方法利用了来自Bugzilla Eclipse项目的数据，并且采用了在问题解决之前可用的特征来提高预测准确性。我们的方法采用情感分析技术以得出情感性评分和正负面情绪分类，同时结合使用BERTopic模型提取的优先级和话题作为卷积神经网络（CNN）和多层感知机（MLP）的输入特征。 研究结果表明，将BERTopic与情感分析相结合可以在某些性能指标上提升模型表现。此外，平衡模型输入以增强其实用性虽在大多数情况下会导致准确性显著降低，但这是必要的步骤。为了实现主要目标——预测解决时间、修复时间和缺陷最终状态，我们采用了二元分类和精确时间值预测的方法，并允许对它们的预测效果进行比较评估。 结果表明，情感分析能够作为预测软件缺陷最终结局的有效工具，特别是在确定该问题是否会被修复方面尤其有效。然而，在将缺陷归类为更复杂或非传统的结果类别时，其效用显得相对较低。
URL
https://arxiv.org/abs/2504.15972
