Bibliographic record
Abstract
Test cases play an important role in testing and debugging software. Smaller tests are easier to understand and use for these tasks. Given a test that demonstrates a bug, test case reduction finds a smaller variant of the test case that exhibits the same bug. Classically, one of the challenges for test case reduction is that the process is slow, often taking hours. For hierarchically structured inputs like source code, the state of the art is Perses, a recent grammar aware and queue driven approach for test case reduction. Perses traverses nodes in the abstract syntax tree (AST) of a program (test case) based on a priority order and tries to reduce them while preserving syntactic validity. In this paper, we show that Perses’ reduction strategy suffers from priority inversion, where significant time may be spent trying to perform reduction operations on lower priority portions of the AST. We show that this adversely affects the reduction speed. We propose , a technique for priority aware test case reduction that avoids priority inversion. We implemented and evaluated it on the same set of benchmarks used in the Perses evaluation. Our results indicate that compared to Perses, is able to reduce test cases 1.3x to 7.8x faster and with 46% to 80% fewer queries.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".