arXiv subjects

Natalia Garanina

Publications and source records attributed to Natalia Garanina.

3 recordsLinked to original sources

arXiv2025

Developing controlled natural language for formal specification patterns using AI assistants

Using an AI assistant, we developed a method for systematically constructing controlled natural language for requirements based on formal specification patterns containing logical attributes. The method involves three stages: 1) compiling a generalized natural language requirement pattern that utilizes all attributes of the formal specification template; 2) generating, using the AI assistant, a corpus of natural language requirement patterns, reduced by partially evaluating attributes (the developed prompt utilizes the generalized template, attribute definitions, and specific formal semantics of the requirement patterns); and 3) formalizing the syntax of the controlled natural language based on an analysis of the grammatical structure of the resulting patterns. The method has been tested for event-driven temporal requirements.

cs.SE↗

arXiv2023

Auto-Tuning High-Performance Programs Using Model Checking in Promela

The paper combines research approaches that traditionally have been disjoint: 1) model checking as used in formal verification of programs, and 2) auto-tuning as often used in high-performance computing. Auto-tuning frameworks optimize parallel programs by finding the optimal values of the performance-critical parameters -- so-called tuning parameters -- for a particular high-performance architecture and input data size. As there are many parameters influencing program's performance, finding the optimal parameter configuration is a hardly manageable task even for experts. Auto-tuning automates this process, but it is often time-consuming. We apply model checking for accelerating auto-tuning by using a counterexample constructed during the verification of the optimality property of the program. We describe in detail an implementation of our approach for programs written in OpenCL -- the standard for programming modern high-performance architectures -- using the model representation language Promela and the popular SPIN verification tool, and we report experimental results for an application use case.

cs.DC↗

arXiv2014

An Approach to Model Checking of Multi-agent Data Analysis

The paper presents an approach to verification of a multi-agent data analysis algorithm. We base correct simulation of the multi-agent system by a finite integer model. For verification we use model checking tool SPIN. Protocols of agents are written in Promela language and properties of the multi-agent data analysis system are expressed in logic LTL. We run several experiments with SPIN and the model.

cs.AI↗