Towards a Unified Metrics Suite for JUnit Test Cases.
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
This paper aims at proposing a unified metrics suite that can be used to quantify different perspectives related to the code of JUnit test cases. We extended existing JUnit test case metrics by introducing two new metrics. We analyzed the code of JUnit test cases of two open source Java software systems (ANT and JFREECHART). We used in total five metrics. We used the Principal Component Analysis (PCA) method in order: (1) to better understand the underlying orthogonal dimensions captured by the suite of unit test case metrics, and (2) to find whether the metrics are independent or are measuring similar structural aspects of the JUnit test code. Overall, results show that: (1) the new introduced unit test case metrics are relevant, (2) the studied unit test case metrics are not independent, and (3) the best subset (a couple) of unit test case metrics that maximizes the variance varies from one system to the other. The new introduced metrics are, however, each in the best subset of unit test case metrics that provide the best independent information that maximizes the variance for each system.
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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 | 0.001 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it