Best Practice Ontology for ITEs

• Best Practice Ontology for ITEs is a structured formalism that defines best practice guidelines, context dimensions, and violation criteria for issue tracking in software engineering.
• The ontology integrates empirical methods, including practitioner interviews and archival analysis, to validate actionable recommendations and ensure contextual accuracy.
• It supports practical applications by enabling standardized documentation, automated detection routines, and adaptive process improvements across diverse software projects.

A Best Practice Ontology for Issue Tracking Ecosystems (ITEs) is a structured, multifaceted formalism developed to organize, compare, automate, and empirically validate best practices in software engineering organizations that use issue tracking systems. The ontology addresses persistent challenges in diverse ITEs—such as inconsistent problem framing in prior research, complex contextual variation in real-world environments, and the lack of a unified framework for documenting “best practice” solutions—by providing a context-rich, systematically organized schema. Below is an in-depth, encyclopedic overview of its conception, structure, methodological underpinnings, applications, and theoretical significance (2507.06704).

1. Ontology Structure and Main Dimensions

The Best Practice Ontology for ITEs is composed of five principal sections, each defined by precise dimensions to allow granular, flexible modeling:

1. Meta: Captures essential provenance attributes, including the Name (a unique, memorable label for each Best Practice) and Source (a traceable reference, typically a research article or industrial documentation).
2. Summary: Encompasses Objective (the aspirational outcome, e.g., “ensure bug report completeness”) and Motivation (the underlying rationale and expected benefits).
3. Recommendation: Specifies actionable guidance through Process (the step-by-step or procedural prescription for teams) and ITS Recommendations (tool- or configuration-level advice relevant to the issue tracking system).
4. Context: Explicitly models the situational parameters critical for the practice’s applicability, with subdimensions such as Stakeholder Benefits, Stakeholder Costs, ITS Scope (the breadth of the recommendation’s effect, from single issues to system-wide), Issue Types affected, Inclusion Factors (promotive conditions), and Exclusion Factors (contraindications or boundaries for application).
5. Violation: Outlines what constitutes non-compliance, including Smells (detectable anti-patterns or indicators), Consequences (negative impacts), Causes (root factors for occurrence), and Algorithmic Detection (formalized detection logic, including pseudocode or automation guidelines).

The ontology’s structure is presented using LaTeX longtables and custom command macros for clarity and repeatable presentation; for instance, \ontHeaderMain{...}, \ontHeaderSecondary{...}, and \ontContent{...} provide semantic markup for each facet in the formal documentation.

2. Methodological Development

Development of the ontology synthesized qualitative and quantitative empirical methods:

• Practitioner Interviews: Semi-structured interviews with 26 participants from diverse roles and countries established the broad and context-dependent spectrum of ITE challenges, particularly highlighting the variability in how ITS tools are used, organizational priorities, and pain points such as workflow bloat or incomplete documentation.
• Archival Analysis: A comprehensive paper of 16 public Jira repositories, collectively comprising millions of issues and their historical evolutions, furnished fine-grained quantitative evidence. Analyses evidenced, for example, that certain fields (like environment) are neglected in up to 90% of bug reports while others (such as priority) are almost ubiquitously populated. Such observations inform the specificity and empirical realism of the ontology’s recommendations and violation criteria.
• Thematic and Iterative Refinement: The construction process adhered to inductive thematic analysis (informed by guides such as Nickerson et al.), iteratively refining dimensions through repeated literature review, empirical cross-checks, and integration of both theory-driven and practice-derived insights.

Formal structure and linkage to higher-level SE constructs are depicted using UML diagrams in the ontology’s documentation.

3. Addressing Challenges in Issue Tracking Ecosystems

ITE challenges motivating the ontology include:

• Information Overload and Incompleteness: Explicit modeling of required fields (e.g., “SetBugReportAssignee”, “SetBugReportPriority”) and consequences of omission provides a basis for organizations to identify, track, and automate enforcement of essential information.
• Workflow Complexity/Bloat: The ontology models applicability scope and context—enabling practices to be precisely targeted (e.g., only to specific issue types, roles, or projects) and thus prevents over-generalization or misapplication.
• Inconsistency and Duplication: By articulating common “smells” (such as duplicate bugs or inconsistent use of link types) and automatable detection logic, the ontology supports research and tool-building for continuous conformance checking.
• Context Dependency: Unlike prior work that often implied universality, the ontology’s Context section models detailed inclusion/exclusion factors and cost-benefit tradeoffs, enabling context-sensitive adoption. This reduces the risk of over-prescription and enhances practical adoption and transferability across heterogeneous organizational settings.

4. Practical Applications and Automation

The ontology is designed for both human and automated application:

• Best Practice Catalog: The companion catalog consists of 40 Best Practices, each instantiated and presented using the ontology’s structure. Practitioners can use these as checklists or configuration blueprints, tailoring recommendations according to documented context dimensions.
• Automated Violation Detection: For each practice, algorithmic detection routines are formalized (often in pseudocode or as field-level checks), enabling integration with ITSs. Example applications include flags for “zombie” (outdated and unresolved) issues, checks that force explicit assignment on closure, or automated reminders when critical fields are blank.
• Tool Development and Integration: The ontology’s dimensions support development of analytical and quality assurance tools, such as scripts that extract issue data, classify according to smell definitions, and suggest or enforce remediation actions.

A plausible implication is that the ontology acts as a bridge between research and practice: empirical studies can now use machine-generated traces of conformance and violation to validate the practical effectiveness of Best Practices and refine recommendations further.

5. Formalization, Presentation, and Theoretical Propositions

Formal presentation methods were integral to the ontology:

• LaTeX Structure: Custom table and command macros (\ontHeaderMain, \ontHeaderSecondary, \ontContent) ensure reproducibility, clarity, and machine readability in the ontology’s rendered documentation.
• UML Diagrams: Structural diagrams link the ontology to wider constructs (such as overall ITE architecture and Issue Tracking Quality) for conceptual coherence.

The ontology’s theoretical underpinnings are further summarized in five propositions that articulate foundational expectations:

1. Contextual dimensions promote falsifiability and empirically grounded if–then mappings, expected to increase best practice adoption.
2. Positive framing of recommendations leads to improved compliance.
3. Stakeholder cost-benefit modeling enhances buy-in and support.
4. The multifaceted structure allows for adaptive, context-driven process improvement.
5. Explicit definition of algorithmic detection criteria supports sustainable automation and ongoing validation.

These propositions are intended as research hypotheses for subsequent empirical evaluation and refinement.

6. Impact and Future Directions

The ontology’s introduction serves as a catalyst for aligning research evidence and industrial practice in issue tracking. By providing a rigorously structured, context-aware, and automation-ready model, it addresses prior fragmentation and helps organizations realize measurable process improvement, traceability, and accountability in software engineering.

The explicit mapping of practices, context, and violation scenarios not only facilitates operational improvements (such as reducing issue latency or error rates) but also positions the ontology as a foundation for larger-scale, cross-organizational studies—enabling researchers to empirically test which practices yield positive outcomes in which contexts.

A plausible implication is that continued empirical use and adaptation of the Best Practice Ontology for ITEs will foster the development of a dynamic, evidence-based body of knowledge, fostering both improved automation in ITS tools and more agile, context-sensitive governance of issue tracking processes.