ModelWisdom: An Integrated Toolkit for TLA+ Model Visualization, Digest and Repair
Zhiyong Chen, Jialun Cao, Chang Xu, Shing-Chi Cheung

TL;DR
ModelWisdom is an interactive toolkit that enhances TLA+ model checking by providing visualization, explanation, and repair features using visualization techniques and large language models, thereby improving interpretability and debugging efficiency.
Contribution
It introduces an integrated environment combining visualization, graph optimization, model digest, and repair functionalities to make TLA+ model checking more interpretable and actionable.
Findings
Enhanced visualization with color highlighting and code links.
Effective graph management through folding and structuring.
Automated summaries and explanations of model subgraphs.
Abstract
Model checking in TLA+ provides strong correctness guarantees, yet practitioners continue to face significant challenges in interpreting counterexamples, understanding large state-transition graphs, and repairing faulty models. These difficulties stem from the limited explainability of raw model-checker output and the substantial manual effort required to trace violations back to source specifications. Although the TLA+ Toolbox includes a state diagram viewer, it offers only a static, fully expanded graph without folding, color highlighting, or semantic explanations, which limits its scalability and interpretability. We present ModelWisdom, an interactive environment that uses visualization and large language models to make TLA+ model checking more interpretable and actionable. ModelWisdom offers: (i) Model Visualization, with colorized violation highlighting, click-through links from…
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Taxonomy
TopicsModel-Driven Software Engineering Techniques · Formal Methods in Verification · Software Testing and Debugging Techniques
