Summary of Online Test Synthesis From Requirements: Enhancing Reinforcement Learning with Game Theory, by Ocan Sankur (devine et al.
Online Test Synthesis From Requirements: Enhancing Reinforcement Learning with Game Theory
by Ocan Sankur, Thierry Jéron, Nicolas Markey, David Mentré, Reiya Noguchi
First submitted to arxiv on: 26 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach is proposed to automatically generate test cases for reactive systems from functional requirements specified as automata. The goal is to reach a specific state while ensuring requirement satisfaction and minimizing coverage criteria violations. A Monte Carlo Tree Search (MCTS) algorithm is used, which selects promising inputs based on the automata requirements. By biasing MCTS towards inputs that are likely to lead to requirement satisfaction, the approach accelerates convergence and improves testing performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’re going to explore a new way to create test cases for systems that react to changes. This system takes in rules written as “automata” (like a game) and creates test cases to make sure it works correctly. The goal is to get the system into a certain state while making sure it follows the rules. A special algorithm called Monte Carlo Tree Search helps us find the best inputs to try. By adjusting this algorithm to focus on inputs that are likely to work, we can make testing faster and better. |
