Scalable and Accurate Test Case Prioritization in Continuous Integration Contexts
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: TCP-CI-dataset.tar.gz: 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. TCP-CI-main-dataset.tar.gz: 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.
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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".