Scalable and Accurate Test Case Prioritization in Continuous Integration Contexts
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 dataset is a benchmark of 25 open-source subjects with 21.5k builds and 2.5k failed builds that enables a fair comparison and evaluation of Test Case Prioritization (TCP) techniques. We made our data collection tools available (github.com/Ahmadreza-SY/TCP-CI), which can be used to extend and update the subjects. The description of the structure and files of the dataset can be also found in the documentation of the data collection tool. Please refer to our academic paper, which can be found on arxiv.org/abs/2109.13168, for details on definitions, experiments, and results. Please cite our paper in any published work that uses resources that are provided in this dataset. We provide two compressed files: <strong>TCP-CI-dataset.tar.gz: </strong>This file contains the dataset, source code of the subjects, the build logs, and the results of the experiments which were conducted in our research. In other words, this file includes all the required resources to replicate the study, and therefore its size is significantly large. <strong>TCP-CI-main-dataset.tar.gz: </strong>This file only contains the dataset which is described in our GitHub repository (link).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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