Diagnosing unwanted behaviour in Multi-Agent Systems (MASs) is crucial to ascertain agents' correct operation. However, generation of MAS models is both error-prone and time intense, as it exponentially increases with the number of agents and their interactions. In this paper, we propose a light-weight, automatic debugging-based technique, coined ESFL-MAS, which shortens the diagnostic process, while only relying on minimal information about the system. ESFL-MAS uses a heuristic that quantifies the suspiciousness of an agent to be faulty; therefore, different heuristics may have different impact on the diagnostic quality. Our experimental evaluation shows that 10 out of 42 heuristics yield the best diagnostic accuracy (96.26% on average).
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