TCPGraphix: A Visualization Tool for ML-Powered Test Case Prioritization Data Analysis
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 presents an analysis of the applications and benefits of TCPGraphix, a tool addressing the need for effective visualization within the domain of Test Case Prioritization (TCP). With an intuitive user interface, TCPGraphix offers an integrated and efficient approach to handle and derive insights from the vast quantity of data produced from TCP processes, specifically TCP processes using Machine Learning (ML). Moreover, it incorporates interactive capabilities, enabling users to navigate and comprehend TCP data easily using a blend of existing and new evaluation metrics. This paper provides a comprehensive overview of TCPGraphix’s design, core features, and applications. TCPGraphix effectively tackles critical data analysis hurdles and empowers researchers to extract valuable insights from expansive data sets and their results.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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