2nd workshop on DevOps and software analytics for continuous engineering and improvement
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
The workshop participants focused and discussed the following areas a) techniques, tools, and schemas to mine software repositories including DevOps environments as well as techniques for denoting information extracted from these repositories. Such information includes not only source code but also deployment scripts, configuration files, build specifications, bug reports, version histories, developers comments and other notes; b) techniques to reconcile software system related data, obtained from such different and diverse DevOps sources (e.g. version control systems, bug reporting systems, collaboration tools and testing frameworks); c) static and dynamic software analysis techniques in order to identify and model direct and indirect dependencies in complex systems, with emphasis on micro-services based systems; and d) software analytics techniques in order to assess deployment risks in order to support continuous maintenance and deployment by providing insights on deploy or no-deploy decision making choices. The workshop topics are related to the IBM DevOps Analytics, IBM DevOps Insights, and IBM DevOps Continuous Delivery (Open Toolchain) frameworks.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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