A Framework for Automating the Measurement of DevOps Research and Assessment (DORA) Metrics
Bibliographic record
Abstract
The DevOps Research and Assessment (DORA) metrics have been widely accepted by the software industry as a powerful method to quantify DevOps performance, leading to significant interest in their measurement. Existing proprietary solutions are highly customised, and require specific types of cloud infrastructure, limiting their suitability for projects such as libraries, frameworks, and open source projects. To address this gap, we present a framework which operationalizes the DORA metrics independently of a project’s software development life-cycle or type of deployment. We demonstrate the general applicability of this framework by using it to calculate the throughput and stability of 304 popular open source repositories. We find that the time-series data it produces provides meaningful insights into the trending direction of a project’s recent and retrospective throughput and stability performance, especially when significant changes in metrics are correlated with major events in the project’s history. We conclude with recommendations for augmenting our approach with additional information such as bug criticality when such information is available.
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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".