An empirical study on identifying fault-prone module in large switching system

Software complexity metrics have been shown to be very closely related to the distribution of faults in software. This paper focuses on identification of fault-prone software modules based on discriminant analysis and classification for software that are developed by CHILL language. We define software complexity metrics for CHILL language. The technique is successful in classifying software modules with relatively low error rare. This procedure shows very useful method in the detection of software modules in the fault of programs with high potential.