DevOps Round-trip Engineering: Traceability from Dev to Ops and Back Again
Bibliographic record
Abstract
DevOps engineers follow an iterative and incremental process to develop Deployment and Configuration (D&C) specifications. Such a process likely involves manual bug discovery, inspection, and modifications to the running environment. Failing to update the specifications appropriately leads to technical debt, including configuration drift, snowflake configuration, and erosion across environments. Despite the efforts that DevOps teams put into automating operation work, there is a lack of tools to support the development and maintenance of D&C specifications. In this paper, we propose TORNADO, a two-way Continuous Integration (CI) framework (i.e., Dev→Ops and Dev←Ops) that automatically updates D&C specifications when the corresponding system changes, enabling bi-directional traceability of the modifications. Panorama extends the concept of CI, integrating operations work into development by committing code corresponding to manual modifications. We evaluated Panorama by implementing a proof of concept using Terraform templates, OpenStack and CircleCI, demonstrating its feasibility and soundness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".