Open AccessSoftware Fault Location of CNC System Based on Similar Path Set and Artificial Neural Network
Author(s) -
Xiuhua Yuan,
Yi-qiang Wang,
Yan Gu
Publication year - 2013
Publication title -
advances in mechanical engineering/advances in mechanical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.318
H-Index - 40
eISSN - 1687-8140
pISSN - 1687-8132
DOI - 10.1155/2013/357308
Subject(s) - computer science , path (computing) , artificial neural network , software , fault (geology) , set (abstract data type) , software system , real time computing , artificial intelligence , operating system , seismology , programming language , geology
The assurance of software reliability of the CNC system is difficult to realize with the continuous increase in the computational complexity and software scale of the CNC system. Therefore, there is an increasing demand for efficient methods that are able to locate the defect codes quickly and accurately. This study proposes a practical fault location method which is based on the similar path set and artificial neural network (ANN). The detailed fault location process involves the following steps: (1) according to the execution information recorded by the monitor, the failed path is generated; (2) the similar path set is obtained from the control flow graph and the failed path; the ANN is trained by the learning sample which is composed of the failed path and the similar path set; (3) the defect code is judged by ANN. The results of the fault location experiment on the interpolation module showed that the proposed method located the software fault quickly and accurately without depending on the experience and intuition of maintainers