Relations as an abstraction for BDD-based program analysis
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
In this article we present Jedd, a language extension to Java that supports a convenient way of programming with Binary Decision Diagrams (BDDs). The Jedd language abstracts BDDs as database-style relations and operations on relations, and provides static type rules to ensure that relational operations are used correctly. The article provides a description of the Jedd language and reports on the design and implementation of the Jedd translator and associated runtime system. Of particular interest is the approach to assigning attributes from the high-level relations to physical domains in the underlying BDDs, which is done by expressing the constraints as a SAT problem and using a modern SAT solver to compute the solution. Further, a runtime system is defined that handles memory management issues and supports a browsable profiling tool for tuning the key BDD operations. The motivation for designing Jedd was to support the development of interrelated whole program analyses based on BDDs. We have successfully used Jedd to build Paddle, a framework of context-sensitive program analyses, including points-to analysis and call graph construction, as well as several client analyses.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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