Proceedings of the International Workshop on Applications of Neural Networks to Telecommunications

Identifying Fault-Prone Software Modules Using Connectionist Networks

N. Karunanithi

Room 2E-378, Bellcore 445, South Street, Morristown, NJ [e-mail]

Abstract

Static complexity metrics are often used to measure the functional complexity of software modules. Classifying software modules, based on their static complexity measures, into different fault-prone categories is an important problem in software engineering. This research investigates the applicability of neural network classifiers for identifying fault-prone software modules using a data set from a commercial software system. A preliminary empirical comparison is performed between a minimum distance based Gaussian classifier, a perceptron classifier and a multilayer ...