TracerX: Pruning Dynamic Symbolic Execution with Deletion and Weakest Precondition Interpolation (Competition Contribution)
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
Abstract Dynamic Symbolic Execution (DSE) is an important method for the testing of programs. The major advantage of DSE is its path-by-path exploration of the program execution space. However, this often leads to the path explosion problem. To address this issue, a method of abstraction learning has been used. The key step here is the computation of an interpolant to represent the learned abstraction. In Test-Comp 2024, we use two different approaches of interpolant generation viz., Deletion Interpolation and Weakest Precondition Interpolation. The former is our more stable and mature system and briefly discussed in [8]. In this paper, we present the latter approach which is the heart of TracerX. In general, the Weakest Precondition (WP) is the ideal (most general) interpolant. However, WP is intractable to compute and is exponentially disjunctive. A major challenge is to obtain a conjunctive approximation of the WP. Therefore, we generate an approximation of the WP.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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