Bibliographic record
Abstract
DevOps, with its increasing prevalence in both industry and academia, has evolved into various DevOps variants (namely XOps) to address emerging technological and operational challenges. However, this proliferation has created confusion and a lack of clarity about the systematic understanding of these XOps and their interrelationship in the DevOps landscape, leading to fragmented knowledge and application. This research seeks to construct a comprehensive picture of the existing DevOps landscape, clarifying the nature and nuances of various XOps, to guide effective future studies and implementations. Utilizing Multivocal Literature Review (MLR), 80 gathered documents are thoroughly examined from throughout the whole community, encompassing both white and grey literature, to map the DevOps landscape. Our review systematically discovered 38 XOps terms and 13 well-studied XOps including AIOps, BizDevOps, CloudOps, DataOps, DevSecOps, FinOps, GitOps, MLOps, ModelOps, NetDevOps, NoOps, SecDevOps and TwinOps. We provided dictionary-like resource that elucidates the core concepts and main ideas associated with each XOps. An in-depth understanding of intricate evolution from DevOps to XOps is delved into, supplemented by the research of relationships between XOps and various technological enablers as well as relationships between XOps and organizational teams, contributing to the ongoing dialogue surrounding their application and evolution. This paper provides a foundational understanding of the DevOps landscape including open issues and challenges, current and future trends, assisting both researchers and practitioners in navigating this complex field. It establishes a platform for further research and practical applications in the evolving field of DevOps and XOps.
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 | 0.033 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.016 | 0.025 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".