  Systems Theoretic Process Analysis (STPA) is a systematic approach for hazard
analysis that has been used across many industrial sectors including
transportation, energy, and defense. The unstoppable trend of using Machine
Learning (ML) in safety-critical systems has led to the pressing need of
extending STPA to Learning-Enabled Systems (LESs). Although works have been
carried out on various example LESs, without a systematic review, it is unclear
how effective and generalisable the extended STPA methods are, and whether
further improvements can be made. To this end, we present a systematic survey
of 31 papers, summarising them from five perspectives (attributes of concern,
objects under study, modifications, derivatives and processes being modelled).
Furthermore, we identify room for improvement and accordingly introduce
DeepSTPA, which enhances STPA from two aspects that are missing from the
state-of-the-practice: (i) Control loop structures are explicitly extended to
identify hazards from the data-driven development process spanning the ML
lifecycle; (ii) Fine-grained functionalities are modelled at the layer-wise
levels of ML models to detect root causes. We demonstrate and compare DeepSTPA
and STPA through a case study on an autonomous emergency braking system.
