Developing industry-wide standards and ensuring producers of mission-critical systems comply with them is crucial to fostering consumer acceptance. Producers of such systems can rely on assurance cases to demonstrate to regulatory authorities how they have complied with such standards to help prevent system failure, which could result in fatalities and environmental damage. In this paper, we introduce SmartGSN, an innovative online tool that relies on Large Language Models to (semi-)automate the management of assurance cases complying with GSN—a very popular notation to graphically represent assurance cases. The evaluation of SmartGSN demonstrates its strong capability to detect assurance case patterns within the assurance cases manually created for five systems spanning several application domains. SmartGSN is accessible online at [ https://smartgsn.vercel.app ], and a demonstration video can be viewed at [ https://youtu.be/qLrTHf-SZbM ].

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SmartGSN: An Online Tool to Semi-automatically Manage Assurance Cases

  • Oluwafemi Odu,
  • Daniel Méndez Beltrán,
  • Emiliano Berrones Gutiérrez,
  • Alvine B. Belle,
  • Gerhard Yu,
  • Melika Sherafat

摘要

Developing industry-wide standards and ensuring producers of mission-critical systems comply with them is crucial to fostering consumer acceptance. Producers of such systems can rely on assurance cases to demonstrate to regulatory authorities how they have complied with such standards to help prevent system failure, which could result in fatalities and environmental damage. In this paper, we introduce SmartGSN, an innovative online tool that relies on Large Language Models to (semi-)automate the management of assurance cases complying with GSN—a very popular notation to graphically represent assurance cases. The evaluation of SmartGSN demonstrates its strong capability to detect assurance case patterns within the assurance cases manually created for five systems spanning several application domains. SmartGSN is accessible online at [ https://smartgsn.vercel.app ], and a demonstration video can be viewed at [ https://youtu.be/qLrTHf-SZbM ].