Bibliographic record
Abstract
by Zhou, Zhang, Hagenbuchner, Tse, Kuo, and Chen, tests search engines.A major problem that is addressed is the 'oracle function', which for these applications means how can we know whether the search results are 'correct' (recommended by Byoungju Choi).The second, Automated Verification and Testing of User-interactive Undo Features in Database Applications, by Ngo and Tan, addresses the problem of testing the ability for users to undo operations.A correspondence between program statements that raise erroneous effects and program statements that can undo those effects is reported and is used to develop a verification technique (recommended by Shaoying Liu).The third, Testing Aspect-oriented Programs with Finite State Machines, by Xu, El-Ariss, Xu, and Wang, reports that aspect-oriented programs have new kinds of faults, which they call aspect faults.This observation is used to develop a test strategy to detect these kinds of faults (recommended by Sudipto Ghosh).The journal held its third editorial board meeting at the Fifth International Conference on Software Testing, Verification, and Reliability (ICST) in Montreal.It was a good meeting with our publishing editor from Wiley and about a dozen members of the board.This meeting is important to resolve issues, discuss strategic directions for the journal, and plan for future directions.Our most important decision was to institute two yearly awards for the journal.The first will be a Best Paper of the Year award.
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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.360 | 0.269 |
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".