Software developers or contributors report issues related to bugs, errors, and missing documentation during community-based software development. These issues are treated as feedback and are crucial to enhancing software new features, documentation, and quality. If software issues are not being addressed with a correct developer, software quality degrades and is unable to use in the end. Hence, it is essential to analyze the software issue-related artifacts to understand the behavior of the software. This paper investigates the performance of the proposed issue-related artifacts mining tool G-Issue with other state-of-the-art tools. We also investigate issue lifetime and evolution of issues over time among well-known and maintained repositories. The results show that G-Issue is faster in mining issue-related artifacts but takes more memory than general Python API during mining issue mining. The results depict that we can prioritize issues based on issue lifetime and evolution. Such results may provide a new horizon about issues that can help in issue management, developer assignment, and quality management. G-Issue URL: https://www.smreza.com/projects/modelmine/issues.php

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