Heuristics for fault diagnosing when testing from finite state machines

Abstract

When testing from finite state machines, a failure observed in the implementation under test (IUT) is called a symptom. A symptom could have been caused by an earlier state transfer failure. Transitions that may be used to explain the observed symptoms are called diagnosing candidates. Finding strategies to generate an optimal set of diagnosing candidates that could effectively identify faults in the IUT is of great value in reducing the cost of system development and testing. In this paper, we investigate fault diagnosis when testing from finite state machines and propose heuristics for fault isolation and identification. The proposed heuristics attempt to lead a symptom to be observed in some shorter test sequences, which helps to reduce the cost of fault isolation and identification. The complexity of the proposed method is analysed. A case study is presented, which shows how the proposed approach assists in fault diagnosis.