Towards a Unified Metrics Suite for JUnit Test Cases.
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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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 | 0.042 | 0.135 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".