Replication Package for PhD Dissertation: "Issue Tracking Ecosystems: Context & Best Practices" by Lloyd Montgomery

Summary of Artefacts

These artefacts accompany the thesis "Issue Tracking Ecosystems: Context and Best Practices" written by Lloyd Montgomery. These artefacts are mostly scripts that produce scientific results and figures from archival data. Additionally, this repository contains scientific protocols, slide decks, and tabular data of different analyses conducted.

Author and Article Details

Lloyd Montgomery - [e-mail] - [e-mail]

Affiliated with the University of Hamburg in Hamburg, Germany.

Please cite this work as:

Montgomery, Lloyd. "Issue Tracking Ecosystems: Context and Best Practices". PhD Thesis. University of Hamburg, Germany, 2025.

Abstract of the Article:

Issue Tracking Systems (ITSs), such as GitHub and Jira, are popular tools that support Software Engineering (SE) organisations through the management of "issues", which represent different SE artefacts such as requirements, development tasks, and maintenance items. ITSs also support internal linking between issues, and external linking to other tools and information sources. This provides SE organisations key forms of documentation, including forwards and backwards traceability (e.g., Feature Requests linked to sprint releases and code commits linked to Bug Reports). An Issue Tracking Ecosystem (ITE) is the aggregate of the central ITS and the related SE artefacts, stakeholders, and processes—with an emphasis on how these contextual factors interact with the ITS. The quality of ITEs is central to the success of these organisations and their software products. There are challenges, however, within ITEs, including complex networks of interlinked artefacts and diverse workflows. While ITSs have been the subject of study in SE research for decades, ITEs as a whole need further exploration.

In this thesis, I undertake the challenge of understanding ITEs at a broader level, addressing these questions regarding complexity and diversity. I interviewed practitioners and performed archival analysis on a diverse set of ITSs. These analyses revealed the context-dependent nature of ITE problems, highlighting the need for context-specific ITE research. While previous work has produced many solutions to specific ITS problems, these solutions are not consistently framed in a context-rich and comparable way, leading to a desire for more aligned solutions across research and practice. To address this emergent information and lack of alignment, I created the Best Practice Ontology for ITEs. Using this ontology, I curated a catalogue of Best Practices from existing literature, including Timely Severe Issue Resolution, Bug-to-Commit Linking, and Avoid Zombie Bugs. I also collected and created algorithms to automatically detect violations to these Best Practices. Finally, I proposed and evaluated tooling solutions that describe how to integrate these Best Practices into existing development environments.

The findings from this thesis enable a structured approach to improving the quality of ITEs. The Best Practice ontology, catalogue, and algorithms are contributions to researchers interested in understanding and improving ITEs. In practice, the context-aware catalogue and algorithms can be used to identify key areas for improvement, and to automate organisational processes such as maintaining a meaningful backlog and ensuring the completeness of issues.

Description of Artefacts

This artefact is split into different chapters, corresponding to the chapters of the thesis this artefact supports. Each individual chapter folder is self-contained, with its own README.md, INSTALL.md, and requirements.txt (where applicable).

Here I provide an overview of what to expect in each folder:

- `chapter_1`: A figure of the thesis overview, and the `.drawio` file that creates it.

- `chapter_3_7_9`: The interview protocol and Excel file used to perform the analysis. The original analysis was performed in GoogleSheets, so the exported Excel file does not fully reflect the visuals and quality of the analysis performed in Google Sheets.

- `chapter_4_5`: The scripts to analyse the archival data from Jira repositories. These scripts also produce the figures present in these chapters.

- `chapter_6`: A figure of the conceptual model presented in the thesis, and the `.drawio` file that creates it.

- `chapter_8`: The scripts to check for Best Practice violations in the Jira dataset. These scripts also produce the figures present in these chapters.

Licences

The code is licensed under [The MIT License](https://opensource.org/license/MIT) by Lloyd Montgomery.

["Issue Tracking Ecosystems: Context and Best Practices"](http://doi.org/10.5281/zenodo.14669551) and all associated data included in this repository (not the code) © 2025 by [Lloyd Montgomery](http://lloydm.io/) is licensed under [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/)