Mining Beyond the Bools: Learning Data Transformations and Temporal Specifications
Mining specifications from execution traces presents an automated way of capturing characteristic system behaviors. However, existing approaches are largely restricted to Boolean abstractions of events, limiting their ability to express data-aware properties. In this paper, we extend mining procedures to operate over richer datatypes. We first establish candidate functions in our domain that cover the set of traces by leveraging Syntax Guided Synthesis (SyGuS) techniques. To capture these function applications temporally, we formalize the semantics of TSL$_f$, a finite-prefix interpretation of Temporal Stream Logic (TSL) that extends LTL$_f$ with support for first-order predicates and fun...
| Authors | Sam Nicholas Kouteili, William Fishell, Christian Scaff et al. |
|---|---|
| Primary category | cs.LO |
| Published | 5 March 2026 |
| Last updated at source | 5 March 2026 |
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