Using benchmarking to advance research: a challenge to software engineering
Bibliographic record
Abstract
Benchmarks have been used in computer science to compare the performance of computer systems, information retrieval algorithms, databases, and many other technologies. The creation and widespread use of a benchmark within a research area is frequently accompanied by rapid technical progress and community building. These observations have led us to formulate a theory of benchmarking within scientific disciplines. Based on this theory, we challenge software engineering research to become more scientific and cohesive by working as a community to define benchmarks. In support of this challenge, we present a case study of the reverse engineering community, where we have successfully used benchmarks to advance the state of research.
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 | 0.276 | 0.518 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.011 | 0.018 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.028 | 0.055 |
| Open science | 0.010 | 0.019 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".