Towards Bridging Formal Methods and Human Interpretability

Labeled Transition Systems (LTS) are integral to model checking and design repair tools. System engineers frequently examine LTS designs during model checking or design repair to debug, identify inconsistencies, and validate system behavior. Despite LTS's significance, no prior research has examined human comprehension of these designs. To address this, we draw on traditional software engineering and graph theory, identifying 7 key metrics: cyclomatic complexity, state space size, average branching factor, maximum depth, Albin complexity, modularity, and redundancy. We created a dataset of 148 LTS designs, sampling 48 for 324 paired comparisons, and ranked them using the Bradley-Terry model. Through Kendall's Tau correlation analysis, we found that Albin complexity ($τ= 0.444$), state space size ($τ= 0.420$), cyclomatic complexity ($τ= 0.366$), and redundancy ($τ= 0.315$) most accurately reflect human comprehension of LTS designs. To showcase the metrics' utility, we applied the Albin complexity metric within the Fortis design repair tool, ranking system redesigns. This ranking reduced annotators' comprehension time by 39\%, suggesting that metrics emphasizing human factors can enhance formal design interpretability.