How do Community Smells Influence Self-Admitted Technical Debt in Machine Learning Projects?
Bibliographic record
Abstract
Background: Community smells reflect poor organizational practices that often lead to socio-technical issues and the accumulation of Self-Admitted Technical Debt (SATD). While prior studies have explored these problems in general software systems, their interplay in machine learning (ML)-based projects remains largely under-examined. Aims: In this study, we aim to investigate the prevalence of community smells and their relationship with SATD in open-source ML projects, analyzing data at the release level. Methods: We analyzed$\mathbf{1 5 5 ~ M L}$-based systems across multiple releases to examine the prevalence of ten community smell types. Then we detected SATD at the release level and applied statistical analysis to examine its correlation with community smells. Next, we considered the six identified types of SATD to determine which community smells are the most associated with each debt category. Finally, we analyzed how the community smells and SATD evolve over the releases, uncovering project size-dependent trends and shared trajectories. Results: Community smells are found to be widespread, exhibiting distinct distribution patterns across small, medium and large projects. Certain smells, such as Radio Silence and Organizational Silos, are strongly correlated with higher SATD occurrences, while authority- and communication-related smells often co-occur with persistent code and design debt. Temporal analysis revealed shared evolutionary trajectories of smells and SATD, influenced by project size. Conclusion: Our findings emphasize the importance of early detection and mitigation of socio-technical issues to maintain the long-term quality and sustainability of ML-based systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".