{'fraction_non_alphanumeric': 0.010676156583629894, 'fraction_numerical': 0.0, 'mean_word_length': 5.698412698412699, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Inferring operational requirements from scenarios and goal models using inductive learning

Goal orientation is an increasingly recognised Requirements Engineering paradigm. However, integration of goal modelling with operational models remains an open area for which the few techniques that exist are cumbersome and impractical. In particular, the derivation of operational models and operational requirements from goals is a manual and tedious task which is, currently, only partially supported by operationalisation patterns. In this position paper we propose a framework for supporting such tasks by combining model checking and machine learning. As a proof of concept we instantiate the framework to show that progress checks and inductive learning can be used to infer preconditions and hence to support derivation of operational models.