Mining Issue Trackers: Concepts and Techniques

These artefacts accompany the "Mining Issue Trackers: Concepts and Techniques" chapter within the "Handbook of Natural Language Processing for Requirements Engineering" book. The book chapter includes a "Use Cases" section where natural language processing (NLP) techniques are applied to issue tracker data from Montgomery et al. [1]. The JupyterNotebooks in this replication package can be used to follow along with the use cases in the chapter.

Lloyd Montgomery - lloyd.montgomery@uni-hamburg.de
Dr. Clara Lüders - clara.marie.lueders@gmail.com
Prof. Dr. Walid Maalej - walid.maalej@uni-hamburg.de

All authors are affiliated with the University of Hamburg in Hamburg, Germany.

Please cite this work as:

Montgomery L, Lüders C, Maalej W. of Part, "Mining Issue Trackers: Concepts and Techniques," in "Handbook of Natural Language Processing for Requirements Engineering", 1st Edition, Ferrari A, Deshpande G. Eds. Springer Nature Switzerland AG, Cham, Switzerland, 2024, to appear.

Title of Chapter: "Mining Issue Trackers: Concepts and Techniques"

Abstract of the Chapter: An issue tracker is a software tool used by organisations to interact with users and manage various aspects of the software development lifecycle. With the rise of agile methodologies, issue trackers have become popular in open and closed-source settings alike. Internal and external stakeholders report, manage, and discuss “issues”, which represent different information such as requirements and maintenance tasks. Issue trackers can quickly become complex ecosystems, with dozens of projects, hundreds of users, thousands of issues, and often millions of issue evolutions. Finding and understanding the relevant issues for the task at hand and keeping an overview becomes difficult with time. Moreover, managing issue workflows for diverse projects becomes more difficult as organisations grow, and more stakeholders get involved. To help address these difficulties, software and requirements engineering research have suggested automated techniques based on mining issue tracking data. Given the vast amount of textual data in issue trackers, many of these techniques leverage natural language processing. This chapter discusses four major use cases for algorithmically analysing issue data to assist stakeholders with the complexity and heterogeneity of information in issue trackers. The chapter is accompanied by a follow-along demonstration package with JupyterNotebooks.

[1] Montgomery, L., Lüders, C., Maalej, W.: An alternative issue tracking dataset of public jira repositories. In: Proceedings of the 19th International Conference on Mining Software Repositories. pp. 73–77 (2022)