Automated Generation of Executable Cucumber Scenarios from a RDBMS Schema
Bibliographic record
Abstract
Acceptance testing addresses the user's expectations for a software system by using scenarios that capture the interactions of such users with that system.To bridge the technical gap between users and developers when discussing the specification and implementation of such scenarios, BDD tools (e.g., Cucumber) were introduced to allow the creation of user-readable scenarios (feature files in Cucumber) and of the corresponding code to make these scenarios executable (Step Definitions in Cucumber).A Relational Database Management System (RDBMS) models in a schema the constraints and inter-relationships of the entities of a software system.Our goal in this thesis is to propose a systematic approach to automatically generate parameterized and executable Cucumber scenarios based on the schema of a relational database.Generating stories pertaining to application-level concepts and their relationships allow early conversations between developers and stakeholders without relying on technical database notions such as fields, data types, constraints etc.Having such stories generated entails they can be easily regenerated as such conversations allow the iterative development of the database schema of the system and having such stories executable ensures the implementation of the entities and constraints specified in this schema can have a small set of corresponding tests automatically applied to it.We implement our generation process in the specific context of Java systems that use the popular frameworks Spring Boot and MySQL.We hope that our approach can be eventually generalized to other frameworks.We discuss at length our work via 2 case studies.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".