2nd workshop on DevOps and software analytics for continuous engineering and improvement
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.
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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 | 0.019 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".