A Solver for a Theory of Strings and Bit-Vectors
Bibliographic record
Abstract
We present the Z3strBV solver for a many-sorted first-order quantifier-free theory Tw, bv of string equations, string length represented as bit-vectors, and bit-vector arithmetic aimed at formal verification, automated testing, and security analysis of C/C++ applications. Our key motivation for building such a solver is the observation that existing string solvers are not efficient at modeling the combined theory over strings and bit-vectors. We demonstrate experimentally that Z3strBV is significantly more efficient than a reduction of string/bit-vector constraints to strings/natural numbers followed by a solver for strings/natural numbers or modeling strings as bit-vectors. We also propose two optimizations. First, we explore the concept of library-aware SMT solving, which fixes summaries in the SMT solver for string library functions such as strlen in C/C++. Z3strBV is able to consume these functions directly instead of re-analyzing the functions from scratch each time. Second, we experiment with a binary search heuristic that accelerates convergence on a consistent assignment of string lengths. We also show that Z3strBV is able to detect nontrivial overflows in real-world system-level code, as confirmed against seven security vulnerabilities from the CVE and Mozilla databases.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".