A summary of state-of-the-art in the literature.

Software bug report classification is one of the most significant processes in software development for determining the nature and severity of faults based on their causes and effects. In many projects, software experts implement this process manually, which requires exorbitant time and effort. Although there are a few studies on automatic bug report classification using machine learning techniques, they mainly focus on structured open-source datasets. This paper presents an ensemble learning approach utilizing various multiclass machine learning, text classification, and natural language processing techniques for automated software bug severity classification, with an application in the Persian language. This language, due to its unique characteristics, requires the adoption of different approaches from those applicable to the English language for text classification. The proposed approach utilizes a real bug dataset extracted from a case study containing unstructured bug reports. This dataset contains 4429 bug reports about the software product of the studied company, which is used by thousands of users in government and private organizations. These bug reports were recorded in Persian text by the testing team or software users, and then classified based on their severity through meetings of development team managers in the company. Results demonstrate that the developed appraoch is highly accurate and significantly faster than manual classification, which can dramatically decrease software development time and cost.软件缺陷报告（software bug report）分类是软件开发流程中至关重要的环节之一，其核心在于依据故障的成因与影响，判定故障的本质与严重程度。在诸多项目中，该流程多由软件专家手动完成，需耗费高昂的时间与人力成本。尽管现有少量基于机器学习技术的自动化缺陷报告分类研究，但此类研究大多聚焦于结构化开源数据集。本文提出一种集成学习（ensemble learning）方法，融合多种多分类机器学习、文本分类以及自然语言处理（natural language processing）技术，实现自动化软件缺陷严重程度分类，并将其应用于波斯语场景。波斯语因其独特的语言特性，在文本分类任务中需采用不同于英语的处理方案。本文所提方法采用了一项来自案例研究的真实缺陷数据集，该数据集包含非结构化的缺陷报告。该数据集涵盖4429条针对研究对象公司软件产品的缺陷报告，其用户群体覆盖政府与私营机构的数千名使用者。这些缺陷报告均由测试团队或软件用户以波斯语撰写，随后由公司开发团队经理通过会议形式，依据其严重程度完成人工分类标注。实验结果表明，所提方法不仅分类精度优异，且相较人工分类效率大幅提升，可显著缩短软件开发周期并降低研发成本。