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 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 | 0.023 | 0.093 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| 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".