Bibliographic record
Abstract
There are many "continuous" practices in software engineering, for example continuous integration (CI), continuous delivery (CD), continuous release (CR), and DevOps. However, the maintenance aspect of continuity is rarely mentioned in publication or education. The continuous practices and applications depend on many repositories and artifacts, such as databases, servers, virtual machines, storage, data, meta-data, various logs, and reports. Continuous maintenance (CM) seeks to maintain these repositories and artifacts properly and consistently through automation, summarization, compaction, archival, and removal. For example, retaining builds and test results created by CI consumes storage. An automated CM process can remove the irrelevant artifacts and compact the relevant artifacts to reduce storage usage. Proper CM is essential for applications' long term sustainability. There are two sides of CM: pre-production and post-production. During the pre-production phase, CM maintains the health of the development environments and the relevant processes. Then during the post-production phase, CM maintains the health of the applications. This paper defines CM for developers. CM complements and completes continuous practices.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".