Defeater Cards introduce a new 5W1H-structured documentation method for systematically characterizing defeaters in safety assurance cases, supported by an open repository and demonstrated in cross-domain case studies.
Defeaters and Eliminative Argumentation in Assurance 2.0
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
abstract
A traditional assurance case employs a positive argument in which reasoning steps, grounded on evidence and assumptions, sustain a top claim that has external significance. Human judgement is required to check the evidence, the assumptions, and the narrative justifications for the reasoning steps; if all are assessed good, then the top claim can be accepted. A valid concern about this process is that human judgement is fallible and prone to confirmation bias. The best defense against this concern is vigorous and skeptical debate and discussion in the manner of a dialectic or Socratic dialog. There is merit in recording aspects of this discussion for the benefit of subsequent developers and assessors. Defeaters are a means doing this: they express doubts about aspects of the argument and can be developed into subcases that confirm or refute the doubts, and can record them as documentation to assist future consideration. This report describes how defeaters, and multiple levels of defeaters, should be represented and assessed in Assurance 2.0 and its Clarissa/ASCE tool support. These mechanisms also support eliminative argumentation, which is a contrary approach to assurance, favored by some, that uses a negative argument to refute all reasons why the top claim could be false.
fields
cs.SE 2years
2026 2representative citing papers
BERT embeddings and meta-classifiers trained on 172 expert-annotated defeaters from two assurance cases achieve F1≈0.84 in predicting quality ratings, outperforming the low inter-rater agreement (κ<0.442) between human reviewers.
citing papers explorer
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Defeater Cards: Characterizing and Managing Safety Assurance Case Defeaters
Defeater Cards introduce a new 5W1H-structured documentation method for systematically characterizing defeaters in safety assurance cases, supported by an open repository and demonstrated in cross-domain case studies.
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Automating Quality Assessment with NLP of LLM-Generated Defeaters
BERT embeddings and meta-classifiers trained on 172 expert-annotated defeaters from two assurance cases achieve F1≈0.84 in predicting quality ratings, outperforming the low inter-rater agreement (κ<0.442) between human reviewers.