arXiv subjects

Clara Lüders

Publications and source records attributed to Clara Lüders.

3 recordsLinked to original sources

arXiv2026

Smells Depend on the Context: An Interview Study of Issue Tracking Problems and Smells in Practice

Issue Tracking Systems (ITSs) enable software developers and managers to collect and resolve issues collaboratively. While researchers have extensively analysed ITS data to automate or assist specific activities such as issue assignments, duplicate detection, or priority prediction, developer studies on ITSs remain rare. Particularly, little is known about the challenges Software Engineering (SE) teams encounter in ITSs and when certain practices and workarounds (such as leaving issue fields like "priority" empty) are considered problematic. To fill this gap, we conducted an in-depth interview study with 26 experienced SE practitioners from different organisations and industries. We asked them about general problems encountered, as well as the relevance of 31 ITS smells (aka potentially problematic practices) discussed in the literature. By applying Thematic Analysis to the interview notes, we identified 14 common problems including issue findability, zombie issues, workflow bloat, and lack of workflow enforcement. Participants also stated that many of the ITS smells do not occur or are not problematic. Our results suggest that ITS problems and smells are highly dependent on context factors such as ITS configuration, workflow stage, and team size. We also discuss potential tooling solutions to configure, monitor, and visualise ITS smells to cope with these challenges.

cs.SE↗

arXiv2024

Mining Issue Trackers: Concepts and Techniques

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.

cs.SE↗

arXiv2022

An Alternative Issue Tracking Dataset of Public Jira Repositories

Organisations use issue tracking systems (ITSs) to track and document their projects' work in units called issues. This style of documentation encourages evolutionary refinement, as each issue can be independently improved, commented on, linked to other issues, and progressed through the organisational workflow. Commonly studied ITSs so far include GitHub, GitLab, and Bugzilla, while Jira, one of the most popular ITS in practice with a wealth of additional information, has yet to receive similar attention. Unfortunately, diverse public Jira datasets are rare, likely due to the difficulty in finding and accessing these repositories. With this paper, we release a dataset of 16 public Jiras with 1822 projects, spanning 2.7 million issues with a combined total of 32 million changes, 9 million comments, and 1 million issue links. We believe this Jira dataset will lead to many fruitful research projects investigating issue evolution, issue linking, cross-project analysis, as well as cross-tool analysis when combined with existing well-studied ITS datasets.

cs.SE↗