Improving the Robustness and Efficiency of Continuous Integration and Deployment
Bibliographic record
Abstract
Modern software is developed at a rapid pace. To sustain that rapid pace, organizations rely heavily on automated build, test, and release steps. To that end, Continuous Integration and Continuous Deployment (CI/CD) services take the incremental codebase changes that are produced by developers, compile them, link, and package them into software deliverables, verify their functionality, and deliver them to end users. While CI/CD processes provide mission-critical features, if they are misconfigured or poorly operated, the pace of development may be slowed or even halted. To prevent such issues, in this thesis, we set out to study and improve the robustness and efficiency of CI/CD. The thesis will include (1) conceptual contributions in the form of empirical studies of large samples of adopters of CI/CD tools to discover best practices and common limitations, as well as (2) technical contributions in the form of tools that support stakeholders to avoid common limitations (e.g., data misinterpretation issues, CI configuration mistakes).
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 | 0.091 | 0.523 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".