Using Data Mining Techniques to Generate Test Cases from Graph Transformation Systems Specifications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<title>Abstract</title> Software testing plays a crucial role in enhancing software quality. A significant portion of the time and cost in software development is dedicated to testing. Automation, particularly in generating test cases, can greatly reduce the cost. Model-based testing aims at generating automatically test cases from models. Several model based approaches use model checking tools to automate test case generation. However, this technique faces challenges such as state space explosion and duplication of test cases. This paper introduces a novel solution based on data mining algorithms for systems specified using graph transformation systems. To overcome the aforementioned challenges, the proposed method wisely explores only a portion of the state space based on test objectives. The proposed method is implemented using the GROOVE tool set for model-checking graph transformation systems specifications. Empirical results on widely used case studies in service-oriented architecture as well as a comparison with related state-of-the-art techniques demonstrate the efficiency and superiority of the proposed approach in terms of coverage and test suite size.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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 it