In recent years, error mining approaches have been proposed to identify the most likely
sources of errors in symbolic parsers and generators. However the techniques used
generate a flat list of suspicious forms ranked by decreasing order of suspicion. We
introduce a novel algorithm that structures the output of error mining into a tree (called,
suspicion tree) highlighting the relationships between suspicious forms. We illustrate the
impact of our approach by applying it to detect and analyse the most likely sources of failure …

In recent years, error mining approaches have been proposed to identify the most likely
sources of errors in symbolic parsers and generators. However the techniques used
generate a flat list of suspicious forms ranked by decreasing order of suspicion. We
introduce a novel algorithm that s tructures the output of error mining into a tree (called,
suspicion tree) highlighting the relationships b etween suspicious forms. We illustrate the
impact of our approach by applying it to detect and a nalyse the most likely sources of failure …