DevOps' Shift-Left in Practice: An Industrial Case of Application
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
DevOps aims at unifying software development and operations to improve products and deliver value to customers. However, many organizations adopt DevOps mainly from a traditional perspective, that is, going forward from development to operations. In this paper we present a case of study that illustrates how Carvajal Technology and Services, a software development organization, improved the design of a family of its software products by exploiting operations data. This case of application constitutes a first incursion of the organization into DevOps, exemplifying how the community and companies in industry can also go backwards from operations to development and design, thus realizing the DevOps shift-left concept. The main contributions of this paper are: (i) the analysis of the industrial DevOps application, for which the deployment automation mechanism is crucial to realize the shift-left concept effectively; and (ii) Amelia, the DSL we developed for deploying the different (re)designs to put into operation and gather feedback data rapidly. To evaluate the approach, the organization analyzed this incursion in both directions: from development to operations, on the benefits of deployment automation; and from operations back to development, by improving the throughput of the original design by a factor of five.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it