Defect prioritization is mainly a manual and error-prone task in the current state-of-the-practice. We evaluated the effectiveness of an automated approach that employs supervised machine learning. We used two alternative techniques, namely a Naive Bayes classifier and a Long Short-Term Memory model. We performed an industrial case study with a real project from the consumer electronics domain. We compiled more than 15,000 issues collected over 3 years. We could reach an accuracy level up to 79.36% and we had 3 observations. First, Long Short-Term Memory model has a better accuracy when compared with a Naive Bayes classifier. Second, structured features lead to better accuracy compared to textual descriptions. Third, accuracy is not improved by considering increasingly earlier defects as part of the training data. Increasing the size of the training data even decreases the accuracy compared to the results, when we use data only regarding the recently resolved defects.

在当前的实践中，缺陷优先级划分主要是一项手动且容易出错的任务。我们评估了采用监督式机器学习的自动化方法的有效性。我们使用了两种替代技术，即朴素贝叶斯分类器和长短期记忆模型。我们使用来自消费电子领域的真实项目进行了工业案例研究。我们收集了3年多来收集的15,000多个问题。我们可以达到高达79.36％的准确度，并且我们进行了3次观察。首先，与Naive Bayes分类器相比，长短期记忆模型具有更好的准确性。其次，与文字描述相比，结构化功能可提高准确性。第三，通过将越来越多的早期缺陷视为训练数据的一部分，无法提高准确性。