Issue Tracking Ecosystems: Context and Best Practices

2025-07-09

AI Summary

Current research on Issue Tracking Ecosystems (ITEs) lacks systematic consideration of contextual richness and cross-tool comparability. To address this, we conduct in-depth practitioner interviews and historical archive analysis across major platforms—including GitHub and Jira—revealing strong contextual dependencies of ITEs along organizational goals, process norms, and technology stacks. We propose the first domain-specific ontology for ITEs (ITE-Ontology), uniquely integrating multidimensional contextual factors with cross-tool traceability requirements. This enables context-aware practice mapping and rigorous cross-case comparison. The ontology is extensible and empirically validated: it enhances diagnostic accuracy in tracking quality assessment and supports evidence-based improvement path design. By unifying conceptual models with industrial realities, ITE-Ontology bridges the semantic gap and methodological disconnect between academic ITE research and real-world practice.

Technology Category

• Knowledge Representation and Reasoning: Ontologies
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Constraint Satisfaction and Optimization: Solvers and Tools

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Web Mining and Content Analysis: Topic discovery and tracking
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services

Abstract

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. <... truncated due to arXiv abstract character limit ...>

Problem

• Understanding complexity and diversity in Issue Tracking Ecosystems (ITEs).
• Addressing context-dependent challenges in ITE research and practice.
• Developing aligned solutions through a Best Practice Ontology for ITEs.

Innovation

• Developed Best Practice Ontology for ITEs
• Conducted practitioner interviews and archival analysis
• Focused on context-specific ITE research