Runtime Semantic Discovery for Evolving Action Alphabets
Runtime Verification typically assumes a fixed alphabet of observable actions. This assumption becomes fragile in evolving software systems, where updates, reconfigurations, or architectural changes may rename, split, or fuse actions while preserving the system's high-level intent. In these cases, previously valid monitors and specifications may become misaligned with the running system. In this paper, we study runtime semantic discovery, namely the problem of identifying whether newly observed actions play the same operational role as previously known ones, without reconstructing a global behavioral model. To this end, we propose a local and runtime-computable notion of action similarity based on the neighbor contexts in which actions occur within execution traces. We define a family of k-neighbor equivalences and use them to compare systems whose observable alphabets differ. Our approach is intentionally lightweight: it does not aim to fully characterize behavioral preservation, but rather to identify candidate semantic correspondences between actions in evolving systems. We discuss the expressive power and limitations of neighbor-based equivalence and show how inferred correspondences can support the adaptation of runtime monitors and specifications under selected classes of alphabet evolution.