Software development projects rely on issue tracking systems at the core of tracking maintenance tasks such as bug reports, and enhancement requests. Incoming issue-reports on these issue tracking systems must be managed in an effective manner. First, they must be labelled and then assigned to a particular developer with relevant expertise. This handling of issue-reports is critical and requires thorough scanning of the text entered in an issue-report making it a labor-intensive task. In this paper, we present a unified framework called MaintainoMATE, which is capable of automatically categorizing the issue-reports in their respective category and further assigning the issue-reports to a developer with relevant expertise. We use the Bidirectional Encoder Representations from Transformers (BERT), as an underlying model for MaintainoMATE to learn the contextual information for automatic issue-report labeling and assignment tasks. We deploy the framework used in this work as a GitHub application. We empirically evaluate our approach on GitHub issue-reports to show its capability of assigning labels to the issue-reports. We were able to achieve an F1-score close to 80\%, which is comparable to existing state-of-the-art results. Similarly, our initial evaluations show that we can assign relevant developers to the issue-reports with an F1 score of 54\%, which is a significant improvement over existing approaches. Our initial findings suggest that MaintainoMATE has the potential of improving software quality and reducing maintenance costs by accurately automating activities involved in the maintenance processes. Our future work would be directed towards improving the issue-assignment module.

翻译：软件开发项目依赖问题跟踪系统来管理诸如漏洞报告和功能增强请求等维护任务。这些问题跟踪系统中的新提交报告须进行有效管理：首先需为其添加标签，继而将其分配给具备相关专长的特定开发人员。这一处理过程至关重要，需要对问题报告中的文本进行全面扫描，属于劳动密集型任务。本文提出一个名为MaintainoMATE的统一框架，能够自动将问题报告归入相应类别，并进一步将其分配给具有相关专长的开发人员。我们采用基于Transformer的双向编码器表示（BERT）作为MaintainoMATE的底层模型，以学习用于自动问题报告标注与分配任务的上下文信息。本文研究的框架被部署为GitHub应用程序。我们基于GitHub问题报告进行了实证评估，以验证其为问题报告分配标签的能力。最终F1分数接近80%，与现有最优结果相当。同样，初步评估表明，我们能够以54%的F1分数将相关开发人员分配给问题报告，较现有方法有显著提升。初步发现显示，MaintainoMATE通过精准自动化维护流程中的各项活动，有望提升软件质量并降低维护成本。未来工作将致力于改进问题分配模块。