Towards Modelling Acceptance Tests as a Support for Software Measurement
Bibliographic record
Abstract
The DevOps paradigm emphasizes the need for a measurable feedback loop, starting from requirements and going as far as deployment in an automated way. In this context, a modelling challenge is to leverage the existing requirement engineering approaches to support measurements. Unfortunately, measurement methods are slow and costly by definition, preventing precisely measured requirements from being used in the DevOps loop. As a result, developers have to deal with grossly estimated elements, e.g., using story points promoted by agile methods. Thus, it is not possible to provide better support for the development team. We envision taking advantage of the artifacts that already exist in a DevOps context to provide better support for requirements measurement, making it available in an automated context such as the DevOps one. This paper focuses on the automated analysis of acceptance tests (e.g., expressed using the Gherkin language) to support functional measurement automation in a DevOps context. This proposition is illustrated by a scenario coming from an industrial partner, supporting the identification of four research challenges to be tackled.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".