State Transition Table Error Checking for FSM Modeling
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Solution Overview
Problem
Designing and verifying finite state machine-modeled systems with complex behavioral traits is challenging due to the complexity of states, transitions, and events, which can lead to omissions and inconsistencies in the state transition model, affecting the accuracy and reliability of the system.
Innovation Solution
A software-based methodology that assists users in designing finite state machine models by generating and error-checking state transition tables, using a host computer to receive system traits, auto-generate initial tables, and perform error-checking to identify omitted critical traits, with features like drag-and-drop graphic tools and customizable event queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual design and verification of state transition tables is performed, then flexibility and customization are maintained, but omissions and inconsistencies occur leading to reduced reliability
Solution Approach 1:
The system performs self-verification by automatically checking the populated state transition table against the complete population of possible states and events. The host computer executes error-checking logic that searches for omitted state transitions and validates consistency without requiring external manual verification, thereby improving reliability while managing complexity through automated self-checks.
Solution Approach 2:
The system implements feedback mechanisms by communicating alerts to the user when omissions or inconsistencies are detected in the state transition table. This feedback loop allows the design process to be iterative, where errors are identified and corrected, thereby improving the reliability of the final model while the structured feedback process helps manage the overall complexity.
2Reliability
If automated error-checking is implemented, then omissions and inconsistencies are identified improving reliability, but additional processing time and system complexity are introduced
Solution Approach 1:
The system performs preliminary error-checking during the table population process itself, rather than as a separate post-processing step. The host computer executes error-checking logic that searches for omitted state transitions as the table is being built, allowing early detection and correction of errors before the final table is completed, thereby reducing total verification time while maintaining high reliability.
3Measurement precision
If complete population of all possible states and events is ensured, then model accuracy is improved, but the initial complexity of defining the complete state space increases
Solution Approach 1:
The system segments the complex task of defining the complete state space into manageable components: first defining the population of possible states and events separately, then using these segmented definitions to automatically generate and verify the complete state transition table. This segmentation reduces the initial complexity by breaking down the overwhelming task of complete state space definition into smaller, more manageable steps that can be performed systematically.
Data Source
AI summary
A method for generating a populated state transition table for a finite-state machine (FSM)-modeled system includes receiving system traits via a host computer of a modeling system. The system traits include states, state transitions, and events of the FSM-modeled system. The method includes generating an initial state transition table in response to receipt of the system traits, the table being partially-populated by the system traits. The method additionally includes populating the table in response to user inputs to thereby auto-generate a populated state transition table, and error-checking the populated table using predetermined error-checking criteria, including searching the populated table via error-checking logic of the host computer for an omitted critical trait of the FSM-modeled system. The method also includes communicating an alert to the user in response to the omitted critical trait.