Issue labels are key drivers in software maintenance as they dictate the prioritization, organization, and, ultimately the resolution of encountered issues. Consequently, mislabeling issues results in inefficient prioritization, which compromises the resolution process of these issues. Thus, to increase the accuracy and effectiveness of issue labeling in software maintenance, this paper proposes “Issue-Labeler”: an automated issue labeler plugin for Jira, which utilizes a deep neural language model to predict an issue’s type based on its title and description. Specifically, the plugin would classify an issue into three types: bug, improvement, or new feature. The issue-labeler plugin was implemented by fine-tuning Google’s pre-trained ALBERT language model, using 35,889 labeled issue reports extracted from 77 projects. The plugin showed an average F1-score of 0.75, 0.58, and 0.67, respectively, for the BUG, IMPROVEMENT, and NEW FEATURE issues. The plugin will provide developers with a tool that recommends issue labels to, in turn, optimize the process of tagging and resolving these issues. Video of tool setup and runtime is available: https://www.veed.io/view/069e5331-3e86-4342-a630-1ec27c2d4c7c?panel=share. Tool Webpage and replication package: https://anonymous.4open.science/r/Issues-labeler-1EBD/.