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Heuristics for fault diagnosis when testing from finite state machines
Author(s) -
Guo Qiang,
Hierons Robert M.,
Harman Mark,
Derderian Karnig
Publication year - 2007
Publication title -
software testing, verification and reliability
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.216
H-Index - 49
eISSN - 1099-1689
pISSN - 0960-0833
DOI - 10.1002/stvr.352
Subject(s) - heuristics , identification (biology) , fault (geology) , computer science , finite state machine , isolation (microbiology) , state (computer science) , set (abstract data type) , reliability engineering , fault detection and isolation , test (biology) , algorithm , machine learning , artificial intelligence , engineering , programming language , paleontology , botany , seismology , microbiology and biotechnology , actuator , biology , geology , operating system
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. This paper investigates fault diagnosis when testing from finite state machines and proposes heuristics for fault isolation and identification. The proposed heuristics attempt to lead to a symptom being 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. Copyright © 2006 John Wiley & Sons, Ltd.