USING DEVOPS PARADIGM TO DEPLOY WEB APPLICATIONS
Bibliographic record
Abstract
DevOps paradigm is widely used in industry to develop software faster, deploy high quality frequent releases of features by integrating and harmonizing the Development and IT Operations activities.Industries are taking strategic decisions to remove the barriers that existed between Development and Operational teams by encouraging collaborations among these teams throughout System Development Life Cycle (SDLC).These strategic decisions to implement DevOps paradigm resulted in the development and emergence of large arrays of tool chains to support, monitor, and automate activities of various SDLC stages.In this paper authors attempt to give practical insights on how the using of DevOps can speed up the management, development and deployment process of a simple web application.Widely used DevOps model consisting of eight stages is used to implement the example application.A toolchain consisting of state of arts tools is used at various DevOps stages.A detailed explanation of each tool, including details to their implementation and a short evaluation concludes the study.The results revealed that the usage of DevOps enables to accelerate the development process of web applications, as most steps during the build and testing process can be automated.Especially the outsourcing of operational overhead to an external cloud provider can lead to economic advantages, which will impact the future of software development.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".