DESIGN METHODS FOR DIAGNOSING AND LOCATING ENTANGLED TECHNICAL DEBT IN DEVOPS FRAMEWORKS
Bibliographic record
Abstract
Abstract In the IT landscape, DevOps is the preferred approach for developing and maintaining rapidly evolving systems that require continuous improvements. Yet, DevOps frameworks do not entirely prevent the accumulation of Technical Debt (TD), and under certain circumstances DevOps can even contribute to generating TD. This paper focuses on a specific type of TD, Entangled Technical Debt (ETD), that corresponds to the implicit complexification of a system's design and the appearance of unintentional couplings in its architecture over time. Our work seeks to inform methods for Diagnosing and Locating ETD in DevOps frameworks. Through a research partnership with Ubisoft's IT branch, an experimental case-study was conducted. It takes the form of an assessment of 6 innovative IT projects and a subsequent in-depth architecture analysis of an individual IT system, which enabled the characterization of the mechanisms linking DevOps to ETD. This allowed us to develop and test practical methods for diagnosing and locating ETD in IT 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 | 0.026 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".