issue tracking

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.

This study addresses the slow issue-resolution speed and unclear driving mechanisms in scientific workflow systems (SWS) on GitHub. Leveraging a large-scale empirical analysis of 21,116 real-world issues, we apply survival analysis (Cox regression), multivariate modeling, and descriptive statistics. We present the first systematic characterization of issue-response heterogeneity in SWS, revealing that standardized label usage and explicit issue assignment significantly reduce median issue closure time to 18.09 days—with 68.91% of issues ultimately resolved. Project activity level, contributor diversity, and issue complexity also exert statistically significant effects. Based on these findings, we propose empirically grounded, sustainability-oriented governance recommendations to enhance SWS reliability and foster community trust. The results provide actionable, evidence-based insights for SWS maintainers and open-science infrastructure stakeholders.

Existing bug-fixing frameworks struggle to address the unique challenges posed by AI/ML systems, including non-deterministic behavior, experiment-driven workflows, and the need for coordinated changes across multiple artifacts. Through a qualitative analysis of 100 issue reports and pull requests from TensorFlow, scikit-learn, MLflow, and AutoGPT, this study systematically uncovers core characteristics of AI/ML debugging and repair—namely cross-phase activities, iterative validation, and multi-artifact coordination. The research identifies key obstacles such as reproducibility issues, behavioral non-determinism, and artifact misalignment. Building on these findings, the paper articulates a vision for a tailored bug-fixing framework specifically designed for AI/ML systems, offering an empirical foundation to guide future toolchain development and research in this emerging domain.

Community-driven scientific workflow ecosystems often struggle to sustain themselves due to ambiguous maintenance and user support mechanisms, particularly in cross-platform collaboration and heterogeneous execution environments. This study presents the first cross-platform empirical analysis of the nf-core ecosystem, systematically examining 15,760 GitHub issues, 35,411 pull requests, and 895 forum discussions. By integrating metadata and textual features into predictive models, the research uncovers significant disparities in maintenance and support activities across platforms and highlights weak explicit linkages among them. The findings reveal that issues, pull requests, and forum posts predominantly serve distinct roles—coordinating maintenance, facilitating code integration, and providing user support, respectively. Moreover, issue actionability, diagnostic evidence, and depth of interaction emerge as critical determinants of resolution efficiency.

Prior work lacks empirical characterization of problem-solving processes in software development. Method: Integrating grounded theory coding, sequential pattern mining, and multidimensional statistical analysis on 356 Mozilla Firefox issue reports, this study extracts fine-grained, reusable problem-solving process patterns from collaborative textual artifacts. Contribution/Results: We identify 47 empirically grounded process patterns—challenging the traditional linear assumption by revealing pervasive nonlinearity: 73% of fixes involve iterative backtracking or parallel activities. The resulting process landscape and pattern catalog systematically characterize distributional regularities across issue types, defect categories, and repair durations. This advances understanding of real-world engineering complexity and provides an evidence-based foundation for process optimization, collaborative tool design, and developer support.

This study addresses the lack of effective cross-channel traceability between community forums and issue trackers in open-source software, which hinders collaboration and transparency throughout the feature request lifecycle. Focusing on the Moodle platform, the research integrates empirical analysis of forum and Jira data, semi-structured interviews, and case studies to systematically reveal that cross-channel links are highly sparse—only approximately 3.5% of issues reference forum discussions—and that author roles are distinctly differentiated, with users predominantly initiating forum posts while developers primarily create tracker issues. Furthermore, the conversion process from forum requests to tracked issues is characterized by informality and low responsiveness. The findings underscore critical gaps in tooling support and ambiguous accountability, offering empirical evidence and design implications for improving collaborative infrastructure in open-source ecosystems.

This study addresses the challenge of effectively monitoring early-stage agent systems, where structural flaws often obscure task-level errors. The authors propose a three-dimensional (quality, suitability, efficiency) and three-granularity (intra-run, inter-run, structural) monitoring and triaging framework tailored for low-maturity agent systems. They introduce a novel system maturity staging model based on the coefficient of variation and monitoring granularity, integrated with a severity classification adapted from FMEA to guide human review. The resulting transferable monitoring architecture supports document-driven, multi-stage workflows, enhanced by a synthetic testbed with controlled error injection. Experimental results demonstrate that structural defects significantly mask task-level signals; 97% of issues can be automatically traced, with only 2% requiring human intervention, and each granularity level precisely identifies its corresponding defect type (coefficients of variation: 0.02, 1.25, and 0.00, respectively).

This study addresses the critical issue of documentation inconsistencies in electronic health records (EHRs), which can compromise clinical decision-making and patient safety. The authors propose the first hierarchical ontology framework specifically designed for EHR inconsistencies, capturing a spectrum ranging from strict contradictions to ambiguous discrepancies. They introduce a fine-grained annotation schema structured along four axes: category, section, clinical domain, and inconsistency type. Leveraging this framework, they develop a two-stage large language model pipeline: Gemini 2.5 Pro first identifies candidate inconsistencies, followed by context-anchored validation using Gemini 2.5 Flash. Applied to 3,000 discharge summaries from MIMIC-IV-Note, the pipeline detected 3,460 inconsistencies across 69.7% of records, predominantly involving demographics, allergies, diagnoses, and medications, while also exposing systematic model limitations in temporal reasoning and outpatient medication knowledge.

Enterprise operational workflows are notoriously difficult to automate end-to-end due to their heavy reliance on human intervention and limited adaptability to change. This work proposes the first action-centric workflow graph framework, which achieves automated construction, execution, and evolution through a three-stage pipeline: structured workflow graphs are extracted from human operation traces, executed via multi-agent online traversal, and continuously optimized in a closed loop using an Adaptive Traversal Reinforcement (ATR) mechanism. Integrating large-scale offline graph construction, graph-guided retrieval, and large language model reasoning, the approach was deployed across four cloud database services. It substantially outperforms the Trace-RAG baseline in coverage breadth, factual accuracy, and diagnostic throughput, achieving an expert blind-review score of 4.95 out of 5.

This study addresses the lack of systematic understanding regarding how GitHub Actions workflows are used in real-world scenarios, how developers respond to workflow failures, and how these practices relate to project characteristics. Combining large-scale quantitative analysis of 258,300 workflow runs with qualitative case studies across 21 diverse repositories, this work identifies three typical patterns developers employ to handle workflow failures and uncovers a “configuration–usage gap”—where YAML configurations exist but workflows remain effectively unused. Furthermore, the study empirically validates five hypotheses linking project features to workflow usage intensity, revealing a significant positive correlation between high usage intensity and low failure rates. These findings provide actionable empirical evidence for improving CI/CD practices.