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
Automatically detecting bugs in programs has been a long-held goal in software engineering. Many techniques exist, trading-off varying levels of automation, thoroughness of coverage of program behavior, precision of analysis, and scalability to large code bases. This paper presents the Calysto static checker, which achieves an unprecedented combination of precision and scalability in a completely automatic extended static checker. Calysto is interprocedurally path-sensitive, fully context-sensitive, and bit-accurate in modeling data operations --- comparable coverage and precision to very expensive formal analyses --- yet scales comparably to the leading, less precise, static-analysis-based tool for similar properties. Using Calysto, we have discovered dozens of bugs, completely automatically, in hundreds of thousands of lines of production, open-source applications, with a very low rate of false error reports. This paper presents the design decisions, algorithms, and optimizations behind Calysto's performance.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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