Enhancing spark's contract checking facilities using symbolic execution
Why this work is in the frame
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Bibliographic record
Abstract
Spark, a subset of Ada for engineering safety and security-critical systems, is one of the best commercially available frameworks for formal-methods-supported development of critical software. Spark is designed for verification and includes a software contract language for specifying functional properties of procedures. Even though Spark and its static analysis components are beneficial and easy to use, its contract language is rarely used for stating properties beyond simple constraints on scalar values due to the burdens the associated tool support imposes on developers. Symbolic execution (SymExe) techniques have made significant strides in automating reasoning about deep semantic properties of source code. However, most work on SymExe has focused on bug-finding and test case generation as opposed to tasks that are more verification-oriented such as contract checking. In previous work we have presented: (a) SymExe techniques for checking software contracts in embedded critical systems, and (b) Bakar Kiasan, a tool that implements these techniques in an integrated development environment for Spark. In this paper, we give a detailed walk-through of Bakar Kiasan as it is applied to an industrial code base for an embedded security device. We illustrate how Bakar Kiasan provides significant increases in automation, usability, and functionality over existing Spark contract checking tools, and we present results from performance evaluations of its application to industrial examples.
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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 | 0.000 | 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